Reducing musculoskeletal disorders in iron ore mine operators: A fuzzy-based intervention approach

Abstract

BACKGROUND:

This study focuses on evaluating the exposure to whole-body vibration (WBV) and association of musculoskeletal disorders (MSDs) with various risk factors among dumper operators in the mining industry. Despite the issue’s significance, prior research has been limited.

OBJECTIVE:

The study introduces a novel fuzzy-based approach for identifying, selecting, and prioritizing safety measures to mitigate MSD risks.

METHODS:

Data collection comprised face-to-face interviews, anthropometric measurements, Rapid Upper Limb Assessment (RULA) scoring for posture assessment, and the Nordic Musculoskeletal questionnaire for assessment of MSD prevalence. Multiple linear and logistic regression models were used to analyse the contributing risk factors to MSDs and WBV exposure. These risk factors formed the basis for a practical approach to select appropriate safety measures based on fuzzy based aggregation method of expert’s judgment aimed at mitigating the risk of MSDs.

RESULTS:

The results revealed that the risk factors such as poor work posture, WBV exposure and poor seat design were significantly associated with neck (adjusted odds ratio aOR = 4.81), upper limb and shoulder (aOR = 3.28), upper back (aOR = 5.09), and lower back pain (aOR = 3.67) at p < 0.05. Using these factors to formulate safety measures to reduce MSD risk, the minimization of sharp turns and abrupt changes in elevation in designing the haul roads, scheduled maintenance practices, and ergonomic seat design were found as important safety measures in this study.

CONCLUSION:

Our unique methodological approach in occupational health research could be highly beneficial for tailoring safety measures at the unit level with minimal effort.

Keywords

Musculoskeletal pain occupational health ergonomics WBV exposure surface mining interventions fuzzy set

1 Introduction

Occupational exposure to whole-body vibration (WBV) is a significant concern across various industries, posing potential risks to workers’ health. It has been widely recognized as a contributing factor to the development of musculoskeletal disorders (MSDs) [1 –7]. Industries such as production, construction, agriculture, and mining expose workers to considerable health hazards through WBV exposure [8]. In the mining sector, the extensive use of heavy earth-moving machinery (HEMM) such as tractors, bulldozers, excavators, dumpers, and forklift trucks driven by operators results in the transmission of vibrations, including shocks and jerks, to their bodies. These vibrations primarily arise from the various forces experienced during HEMM operation, such as accelerations, decelerations, lateral swaying, and vertical vibrations [9]. Consequently, the vibrations and shocks transmitted through the vehicle’s seat effect the operator’s back, neck, and shoulders. Prolonged exposure to such mechanical stress can lead to detrimental effects on the spine (bones and intervertebral discs) as well as the soft tissues (muscles and nerves) [10 –15]. Statistical data from the United States Bureau of Labor Statistics reported an incidence rate in 2014 of work-related MSDs of 26.9 per 10,000 full-time employees across all mining sectors [16]. Studies conducted in opencast mines in Finland, Norway, Russia, and Sweden have further demonstrated a higher prevalence of MSDs among vehicle drivers in comparison to other mining personnel [17]. Some studies have also indicated a high prevalence of self-reported musculoskeletal symptoms among mine vehicle operators compared to non-exposed workers [18 –22].

Recent epidemiological investigations shown that prolonged exposure to WBV increases the risk of low back pain (LBP) (especially degeneration of the lumbar spine and intervertebral disc disorders) [17 –22]. Sciatica associated with WBV exposure are recognized as occupational diseases in some countries [23, 24]. Some studies reported that prolonged poor sitting postures (involving especially flexion or torsion of the back or neck) which are also common among workers can result in localized stress and MSDs [25, 26]. The risk of LBP is particularly high among workers exposed to multiple factors, including vibration, inadequate seat design, and operator’s awkward posture [26 –28]. High vibration amplitude intensifies discomfort, induces muscle relaxation and sedation, and make it hard to maintain posture [26]. A few studies have demonstrated the physiological effects of inappropriate posture and WBV on various body parts such as the neck, shoulders, hips, and knees [29, 30]. Driver’s posture during operation is also a potential risk factor as it influences WBV magnitude [29, 30]. Indeed, leaning (forward or backward) while driving increases WBV level as compared to an upright sitting position [30]. Conversely, contact with the backrest has a small impact on WBV magnitude [28].

Surface mines employ dumpers with larger capacities compared to trucks used in other industries. Dumper operations in mines involve repetitive activities including loading, unloading, and loaded and empty travels. Dumpers navigate steep gradients and sharp turns within short distances, leading to repetitive posture changes over brief time. Unfortunately, the influence of awkward postures and WBV exposure on MSDs among dumper operators remains poorly addressed [31, 32]. However, two studies showed the effects of age, weight, seat design, awkward posture, and WBV on MSDs in real working conditions [32, 33].

While efforts have been made to mitigate operators’ WBV exposure through engineering and technical developments [34, 35], concerns regarding health and safety related to MSDs still persist [36]. Identifying and implementing appropriate safety measures to minimize MSDs are crucial, as existing initiatives are often inconsistent, inadequate, or lacking. The absence of these interventions not only poses challenges to workers’ health but also gives rise to social and economic problems for both employees and employers [37]. Knowledge regarding the most effective occupational health and safety interventions for reducing MSDs remains limited [38]. Moreover, in the mining industry, knowledge about safety measures against MSDs and a systematic approach to identifying such measures is even scarce. Therefore, conducting studies aimed at developing measures to address health and safety issues related to WBV exposure is of paramount importance. Efficient identification and effective implementation of interventions can improve operators’ safety behaviour, health and safety.

In order to construct and prioritize safety measures to reduce MSD risks, fuzzy-based intervention approach may be appropriate for surface-mine dumper operators. Indeed, this approach acknowledges that experts may have imprecise/probabilistic beliefs rather than binary judgments. In available intervention design studies, selection of safety measures usually rely on expert’s judgements and their numerical coding, which may fail to capture perceived experts’ judgments. Fuzzy similarity aggregation method (SAM) of expert’s opinion methods offers advantages over conventional expert scoring [39]. The inclusion of uncertainty, allows experts to express their opinions in nuanced manner. Additionally, fuzzy aggregation handles conflicting opinions by assigning appropriate weights to each expert’s input, thereby capturing a broader range of perspectives and avoiding the dominance of a single expert or group. Furthermore, it enhances the robustness against bias by considering multiple viewpoints and reducing the impact of individual biases. By promoting consistency, transparency, and adaptability, fuzzy aggregation provides a comprehensive framework for decision-making that accommodates evolving knowledge and fosters trust in the decision outcomes. However, implementing a fuzzy-based system can be intricate, often requiring advanced knowledge and understanding of fuzzy logic principles. Therefore, complexity, demand for high-level expertise, transparency, and integration challenges are important considerations that need to be addressed for effective implementation.

This study, conducted among dumper operators in Indian iron ore mines, aimed at assessing WBV exposure and associated health risks, potential associations between risk factors and different categories of MSDs, and construct and prioritize safety measures based on their effectiveness in reducing MSD risks using fuzzy-based intervention approach.

2 Methods

2.1 Study mines

This research was conducted over six months in two open-cast iron ore mines in eastern India. Both mines were fully mechanized and had similar infrastructure, safety practices, and work schedules, producing over 10 million tons of iron ore annually. A single company operated the mining operations. The material was extracted from benches (12-meter-high and 25-meter-wide) and loaded onto 100-tonne dumpers using shovels or loaders. These dumpers transported the material to the beneficiation plant. The mines primarily targeted iron ore deposits on hilltops, and the haul roads within the mines were characterized by uneven surfaces, sharp turns, and an average slope of 6 degrees.

2.2 Study design

The study protocol consisted of several key steps. Initially, a formal request was submitted to the mine management, seeking permission for dumper operators’ participation in the research. Following approval, data collection was made through face-to-face interviews using a standardized questionnaire known as the “workers’ response device questionnaire.” The interviews were conducted during the operators’ shift breaks and were administered in the local language to ensure comprehension and facilitate effective responses.

Furthermore, measurements were taken to evaluate the whole-body vibration (WBV) exposure experienced by dumper operators. The study also encompassed the assessment of the operators’ static body dimensions and captured their dynamic postures through video recordings. In total, 65 dumper operators voluntarily participated in the study, representing approximately 93% of the operators employed at the two mines. Operators undergoing on-the-job practical training were excluded from the study. The participants’ age ranged from 27 to 60 years, and their experience level from 3 to 35 years.

Ethical approval for the study was obtained from the Department of Mining Engineering at the Indian Institute of Technology Kharagpur. Informed consent was acquired from each participant, with a clear explanation of the study’s purpose and the extent of their involvement. Personal characteristics, job-related information, self-reported musculoskeletal disorders (MSDs), seat design measurements, and machine-related data were collected as part of the study, including haul road conditions, and dumper speeds, all of which were obtained through on-field measurements. The flow diagram of the study design is presented in Fig. 1.

Fig. 1

Flow diagram of the study design.

2.2.1 Personal factors

To collect personal information, dumper operators filled up a questionnaire that contained information such as the age and weight of operators. The study exclusively included male participants with a range of ages from 27 to 60 years.

2.2.2 Machine-related factors

The dumpers in the study were of the same model from a single manufacturer and were in the age range of 2 to 5 years. All the dumpers were equipped with mechanical seat suspension systems. They were primarily used for transporting iron ore to a pit-head crushing plant with lead distances ranging from 2.5 to 5 km. Additionally, the dumpers were also used for transporting overburden to a subgrade dump with a lead distance of 2 km. Measurements were taken during the field study to assess seat characteristics, including seat height, seat width, seat length, and seat backrest height. These measurements provided important information about the seating arrangements for the dumper operators involved in the study.

2.2.3 Operation-related factors

To assess the impact of poor haul road conditions on operators’ WBV exposure, the condition of the haul roads was evaluated. These roads were characterized by uneven surfaces, including puddles, potholes, debris, and bumps. The terrain also featured hilly areas with sharp turns, resulting in a maximum gradient of 6 degrees. For measuring floor vibrations experienced by operators, a single-axis piezoelectric accelerometer (Model no. Nor-1287, Norsonic, Norway) was securely installed beneath the seat in the dumper cabin. Following ISO 2631-1 : 1997/Amd.1 : 2010 guidelines, a mono-axial accelerometer (Model no. Nor-1287, Norsonic, Norway) was magnetically attached to the floor to ensure proper sensor-surface alignment [40 –43]. These measurements were taken during dumper operation, with the dumper’s speed recorded using a GPS. The International Roughness Index (IRI) was used to quantify haul road roughness [44]. IRI values were calculated for different roads by analyzing floor acceleration data at various velocities and averaging the results. Higher IRI values indicated rougher surfaces, while lower values indicated smoother surfaces. This approach provided a quantifiable assessment of road roughness and facilitated the evaluation of surface conditions across different haul roads.

2.2.4 Ergonomics features

poor seat design

The ergonomic compatibility between seat design and operator anthropometry plays a significant role in determining operator comfort. In this study, the sitting height of dumper operators was measured using the anthropometer and the backrest height of the dumper seats was measured using a measuring tape. By comparing the operator’s sitting height with the seat backrest height, the compatibility of the seat design was assessed. It was observed that the dumper seats had a poor seat suspension system, lacking an adjustable mechanism for upward and downward movement. Additionally, the seat cushions were found to be hard and torn. In cases where the operator’s sitting height exceeded the backrest height, it indicated an improper seat height. Consequently, seats with improper height, inadequate seat suspension, lack of adjustment mechanism, and uncomfortable cushions were classified as poor seat design (Fig. 2).

Posture assessment

Fig. 2

Poor seat design: Jammed seat suspension, poor maintenance, and worn-out seat cushion.

The assessment of the operator’s posture is crucial in determining their comfort levels and potential ergonomic risks [45]. The Rapid Upper Limb Assessment (RULA) method was employed for posture analysis. RULA considers biomechanical and postural load requirements of job tasks, focusing on the neck, trunk, and upper extremities. Operators’ movements and postures were observed during work cycles, utilizing videos and photography. ErgoMaster software facilitated posture assessment by importing digital images, enabling easy storage and retrieval of posture scores [46]. This comprehensive analysis identified inappropriate postures that could contribute to discomfort and ergonomic risks. RULA consists of 3 tables: Table A, Table B and Table C. Table A and Table B scores arm and wrist analysis, and neck, trunk, and leg analysis, and the final RULA score was computed from Table C [46]. These scores were categorized as negligible, low, medium, or high-risk, with urgent attention required for medium and high-risk actions to reduce exposure [47, 48]. Observations were made from the side with a helper’s seat available (Fig. 3).

Fig. 3

Postures adopted during working: Operator’s back and head is not supported.

2.2.5 Assessment of musculoskeletal disorders MSDs

The musculoskeletal disorders (MSDs) were identified using the Nordic Musculoskeletal questionnaire (NMQ) [48, 49]. This validated tool captures self-reported complaints of MSDs in specific body regions, including the neck, upper limbs, upper back, lower back, and knees/legs. The NMQ questionnaire covered demographic information, job characteristics, and details of musculoskeletal discomfort experienced. Participants received assistance to ensure clarity and accurate responses, minimizing misunderstandings.

The NMQ is a standardized and widely used tool in occupational health and ergonomics for the assessment of musculoskeletal symptoms in various body regions [48]. It is designed to investigate the prevalence and incidence of MSDs among workers in different occupational situations [50 –52]. A number of studies have shown that the NMQ has good internal consistency (Cronbach’s Alpha coefficient about 0.83), test-retest reliability, and intraclass correlation (about 0.70) [48 , 50–52].

2.2.6 Measurement of whole-body vibration

Vibration data was collected using a 6-channel vibration meter (Model no. Nor-136, Norsonic, Norway) following ISO-2631-1 : 1997/Amd.1 : 2010 guidelines [40 –43]. It included a triaxial accelerometer (Model no. Nor 1286, Norsonic, Norway) and a vibration analyzer. The accelerometer, with 100-mV/g sensitivity and 2–20 mA supply current, was placed on the dumper seat to measure body vibration. Data were recorded, time-stamped, and stored in the vibration analyzer connected via a 2 m cable. Another monoaxial accelerometer was magnetically attached to the dumper floor beneath the seat. The accelerometers recorded acceleration data within the frequency range of 0–80 Hz. WBV exposure analysis used Microsoft Excel and NorVibraTest software. The mounting of accelerometers adhered to ISO 10326-1 : 2016 guidelines and the vibration meter was designed in compliance with ISO-8041-1 : 2017 standards [42, 43]. The vibration was measured in the x, y, and z axes. NorVibraTest software (Model no. Nor 1038, Norsonic, Norway) analyzed vibration data. Six cycles per operator were collected to represent an 8-hour daily vibration exposure. A cycle included loading, travel, unloading, and waiting, lasting 35–50 minutes. GPS data was used to determine the dumper’s speed.

WBV exposure quantification involved averaging vibration levels using one-third octave bands. Weighting filters were applied based on associated harmful effects in the frequency ranges. Weightings W_d (x, y axes) and W_k (z-axis) were used for the measurement of seated WBV exposure. Daily exposure in terms of A(8) and VDV(8) quantified the 8-hour exposure during a shift. ISO guidelines defined health-guidance caution zone (HGCZ) limits of A(8) at 0.45 m/s² (lower) and 0.9 m/s² (upper) and 8.5 m/s^1.75 (lower) and 17 m/s^1.75 (upper) for VDV(8) [45, 46]. These exposure levels help identify workers who may require specific preventive measures.

2.3 Statistical analysis

The study used multiple linear regression analysis to examine the relationship between independent variables (such as age, weight of operators, years of machine operation, average speed, and poor haul road condition) and the outcome variables A(8) and VDV(8). Musculoskeletal disorders (MSDs) were assessed using binary coding, with a value of 1 indicating the presence of neck pain, upper limb and shoulder pain, upper back pain, lower back pain, and knee and leg pain, while a value of 0 indicated no pain. The exposure to whole-body vibration (WBV) was categorized as either low or high. High WBV exposure was defined as values exceeding the lower limit of 0.45 m s^-2 and 8.5 m s^-1.75 according to ISO-2631 : 1997/Amd.1 : 2010, while values below this limit were classified as low WBV exposure. Similarly, a Rapid Upper Limb Assessment (RULA) score above 4 indicated an awkward posture that placed an operator at a medium or high-risk level, which required immediate management intervention. For operators whose anthropometric sitting height exceeded the backrest height, it was considered a mismatched seat height. As a result, the presence of inadequate height, coupled with jammed seat suspension, absence of adjustment mechanisms, and poor seat cushions was coded as poor seat design. The associations of age, overweight, A(8), VDV(8), poor posture, and poor seat design with various types of MSDs were assessed with crude odds ratios (cOR), adjusted odds ratios (aOR) and 95% confidence interval (95% CI) which were computed using logistic regression models. All tests were two-sided with a significance level of p < 0.05. Statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA).

2.4 Safety measures based on fuzzy similarity aggregation method (SAM)

Safety measures improve safety and health through modifications of practices, including programs, engineering interventions, training, and administrative procedures. However, researchers often underutilize statistical insights on MSDs and associated risk factors for safety measures. This study proposes a quantification-based approach that translates statistical and fuzzy-based model outcomes into practical safety measures. It identifies areas for improvement and selects necessary measures to achieve those improvements. The approach is demonstrated in the context of addressing MSD risk among dumper operators.

(1). Significant risk factors for MSDs were initially identified through statistical analysis of field data. Subsequently, field-based observations and discussions with mine officials facilitated to identify the risk factors closely associated with MSDs. However, the significant risk factors identified in this study are often intricate and involve multiple aspects within a larger risk factor To address the complexity of these risk factors, they were divided into multiple facets. This division allowed for the identification of multiple aspects within each risk factor, enabling the formulation of specific safety interventions for each aspect. Similarly, all significant risk factors were divided to identify areas that need improvement, referred to as improvement targets (ITs). For each IT, specific safety measures (SMs) were identified to facilitate the desired improvements.

(2). To determine the most suitable safety measures (SM) for each improvement target (IT), this study relied on the practical expertise of industry professionals and academic experts within the mining community. Initially, multiple potential SMs were identified for each IT. Through an opinion survey, the knowledge and experience of 10 experts were incorporated to assess and rate each SM for a specific IT using linguistic labels such as “Strongly ineffective”, “Ineffective”, “Somewhat ineffective”, “Neither effective nor ineffective”, “Somewhat effective”, “Effective” and “Strongly effective” based on practicality and ease of implementation. Each of these labels was assigned appropriate triangular fuzzy numbers as shown in Table 6. Fuzzy numbers are fuzzy sets that are defined on set of real number R [53]. This study used the triangular fuzzy numbers as they offer simplicity without compromising on accuracy [54]. A triangular membership function is shown in Fig. 4, where $μ_{\tilde{i}} (x)$ and μ_j (x) represents the membership function of expert’s judgment E_i and E_j. This study uses the similarity method for aggregating (Fig. 4) the experts’ opinions [55]. This method quantifies the agreement degree (AD) between two experts E_i and E_j [56], denoted by AD_ij, and is defined as given by Equation (1). Afterward, an agreement matrix (AM) was formulated to capture the consensus among experts for each safety measure corresponding to improvement target, which is given by the matrix in Equation (2).

Fig. 4

Visualization of the fuzzy similarity aggregation method.

$A D_{ij} = \frac{A_{\min}}{A_{\max}}$ (1) where

A_min is area projected on real axis by min ( $μ_{\tilde{i}} (x)$ , μ_j (x)).

A_max is area projected on real axis by max ( $μ_{\tilde{i}} (x)$ , μ_j (x)). $AM = [\begin{matrix} \begin{matrix} 1 & A D_{12} \\ ⋮ & ⋮ \end{matrix} & \begin{matrix} \dots & \begin{matrix} A D_{1 j} & \begin{matrix} \dots & A D_{1 n} \end{matrix} \end{matrix} \\ ⋱ & \begin{matrix} ⋮ & \begin{matrix} ⋱ & ⋮ \end{matrix} \end{matrix} \end{matrix} \\ \begin{matrix} A D_{i 1} & A D_{i 2} \\ \begin{matrix} ⋮ \\ A D_{n 1} \end{matrix} & \begin{matrix} ⋮ \\ A D_{n 2} \end{matrix} \end{matrix} & \begin{matrix} \dots & \begin{matrix} A D_{ij} & \begin{matrix} \dots & A D_{in} \end{matrix} \end{matrix} \\ \begin{matrix} ⋱ \\ \dots \end{matrix} & \begin{matrix} \begin{matrix} ⋮ \\ A D_{nj} \end{matrix} & \begin{matrix} \begin{matrix} ⋱ & ⋮ \end{matrix} \\ \begin{matrix} \dots & 1 \end{matrix} \end{matrix} \end{matrix} \end{matrix} \end{matrix}]$ (2)

The average agreement degree (AAD_i) of the i^th expert was calculated by Equation (3). The relative degree of agreement (RAD_i) of the i^th expert was calculated by Equation (4). $AA D_{i} = \frac{1}{n - 1} \sum_{\begin{matrix} j = 1 \\ j \neq i \end{matrix}}^{n} A D_{ij}$ (3) $RA D_{i} = \frac{AA D_{i}}{\sum_{i = 1}^{n} AA D_{i}}$ (4)

The final fuzzy number $\tilde{E_{k}}$ for k^th intervention was obtained using Equation (5). The fuzzy number was then defuzzified using the centroid approach to obtain the score for the safety measure of a particular improvement target. $\tilde{E_{k}} = \sum_{i = 1}^{n} RA D_{i} ⊙ \tilde{E_{i}}$ (5) where

⊙ is the fuzzy multiplication

${\tilde{E}}_{i}$ is fuzzy number for i^th expert

The SM with the highest score was selected as the optimal intervention for each corresponding IT. Since a single SM was chosen for each IT, they maintain a one-to-one correspondence.

(3). Finally, the selected safety measures (SMs) were prioritized based on their effectiveness in addressing the corresponding improvement targets (ITs) and the impact of the ITs on MSDs. The effectiveness score of a particular SM reflects its efficiency in achieving the improved target, whereas the impact rating of an IT indicates its significance in the occurrence of MSD.

The following stepwise methodology outlines the calculation of the impact of each IT on MSD risk, the effectiveness of each SM, and subsequently determining the priority weights of the safety measures.

(a) Computation of impact rating (M_i) for ITs: The impact rating for each IT was calculated using the response score of dumper operators from questionnaire survey items or measurements, and the maximum value of the coefficients from multiple logistic regression (Table 5) as given in Equation (6). $\begin{matrix} M_{i} = probability score \times \\ (Max . value of Logistic regression coefficient), \\ M_{i} = P_{i} \times β_{\max} \end{matrix}$ (6) where,

Probability score (P_i) is the percentage of the average score for the survey questionnaire item related to ith IT divided by the cumulative score for all the Its and is calculated using Equation (7). $probability score = P_{i} = (R_{i} / \sum R_{i}) * 100$ (7) where,

N is the total number of IT formed based on significant determinants.

R_i is calculated by taking the average of the response scores of all the operators, shift manager, shift in charge, and safety officer for the survey questionnaire item(s) related to ith IT. $R_{i} = (Cumulative response score for i^{th}$ $IT / numbers of total persons) .$

High P_i denotes that a higher number of persons want attention to be paid to the ith IT. The regression coefficient represents the strength of the association between a risk factor and MSD. So, impact rating M_i (probability X effect) represents risk score (probability X consequence) for ith IT. The impact rating is standardized using the cumulative score of the impact rating as given in Equation (8). $m_{i} = (M_{i} / \sum M_{i})$ (8)

(b) Effectiveness score (E_i) of SM: The effectiveness score for each SM was calculated by the fuzzy aggregation method through an expert opinion survey. Effectiveness scores (E_i) represent the practical effectiveness of the safety measure in reducing MSD risk.

(c) Computation of priority weights (p_i) of SM: The priority weights of interventions were calculated using a standardized importance rating (m_i) and effectiveness score (E_i). Then, the cumulative priority weight for the set of SI was used to calculate standardized priority weights. These are represented by Equations (9) and (10). $p_{i} = m_{i} * E_{i}$ (9) $P_{i} = (p_{i} / \sum p_{i})$ (10)

(d) Standardized priority weight (P_i) is used to rank the SMs. Standardized priority weight denotes the effectiveness of a safety measure and rank denotes that priority should be given to implementation through resource allocation.

3 Results

Table 1 shows that the dumper operators were highly exposed to WBV including shocks. The magnitude of RMSA was much greater in the z-axis with the mean value of 0.48 m s^-2 (range 0.31–0.81 m s^-2) than in the x-axis (mean = 0.21 m s^-2, range = 0.15–0.30 m s^-2) and y-axis (mean = 0.21 m s^-2, range = 0.16–0.27 m s^-2). The mean of vector sum a_wv was 0.63 m s^-2 (range 0.43–0.97 m s^-2). The VDV value was also much higher in the z-axis with a mean value of 5.24 m s^-1.75 (range 2.92–9.25 m s^-1.75) than in the x-axis (mean = 2.37 m s^-1.75, range 1.71–3.36 m s^-1.75) and y-axis (mean = 2.34 m s^-1.75, range 1.63–3.02 m s^-1.75). The mean value of VDV_sum was 5.38 m s^-1.75 (range 3.15–9.31 m s^-1.75). For A (8), 59% of operators were exposed to a value exceeding the lower limit (0.45 m s^-2) of the “Health Guidance Caution Zone” (HGCZ). Similarly, for VDV (8), 90.8% of the dumper operators were exposed to a value above the lower limit of HGCZ (8.5 m s^-1.75). As the magnitude of vibration in the horizontal direction was low compared to the vertical direction, the health risk assessment was carried out based on the dominant axis as laid out in Standard No. ISO-2631-1 : 1997 [40, 41] guidelines. However, ISO states that when vibration in two or more axes is comparable, the vibration total value (root-sum-of-squares) be used as an additional estimate for health risk assessment. Therefore, the vector sum of the vibration value was also computed. Based on the a_wv value, 86% of the operators were found to be in the moderate health risk zone and 14% reached the upper limit signifying the high health risk. The mean of the operator’s age and weight was 46.3 years (SD = 9.68, range = 27–60 years) and 76.4 kg (SD = 8.48, range = 60–92). The years of run of dumpers were in the range of 1.51–5.50 years. The average dumper’s speed was 8.05–52.05 km/h, and the poor-haul-road-condition score was 5.51–14.84. Multiple linear regression of A(8) with the predictor variables revealed that the average speed of the dumpers (β= 0.003; p = 0.026), years of run of the machine (β= –0.020; p = 0.012), and poor haul road condition (β= 0.009; p = 0.002) were significantly associated with WBV exposure. Similarly, years of run of the machine (β= –0.456; p = 0.012) and poor haul road condition (β= 0.179; p = 0.006) were significantly associated with VDV(8). The unusual finding of years of run and WBV exposure needs to be further investigated. These factors were afterwards used in formulating the targets needed to be improved in the area of WBV exposure to enhance safety measures.

Table 1
Occupational whole-body vibration exposure of dumper operators (n = 65)

Mean (SD) Median Skewness Range <ISO lower Within ISO >ISO upper

limit^a (%) limit^a (%) limit^a (%)

Whole body vibration exposure

RMSA

a_wx (anterior-posterior x-axis), ms^–2 0.21 (0.03) 0.21 0.44 0.15–0.30 100.0 0.0 0.0

a_wy (medial-lateral y-axis), ms^-2 0.21 (0.02) 0.21 0.09 0.16–0.27 100.0 0.0 0.0

a_wz (vertical z-axis), ms^–2 0.48 (0.10) 0.47 0.81 0.31–0.81 41.5 58.5 0.0

a_wv (total), ms^–2 0.63 (0.10) 0.62 0.78 0.43–0.97 0.0 96.9 3.1

A(8) (daily exposure), ms^–2 0.48 (0.10) 0.47 0.81 0.31–0.81 41.5 58.5 0.0

VDV exposure

VDV_x (anterior-posterior x-axis), ms^–1.75 2.37 (0.37) 2.36 0.34 1.71–3.36 100.0 0.0 0.0

VDV_y (medial-lateral y-axis), ms^–1.75 2.34 (0.29) 2.36 –0.12 1.63–3.02 100.0 0.0 0.0

VDV_z (vertical z-axis), ms^–1.75 5.24 (1.22) 5.13 1.07 2.92–9.25 96.9 3.1 0.0

VDV_sum (total), ms^–1.75 5.38 (1.18) 5.26 1.09 3.15–9.31 96.9 3.1 0.0

VDV(8) (daily exposure), ms^–1.75 10.90 (2.25) 10.56 1.11 7.32–17.99 9.2 90.8 0.0

Personal and operational factors

Age, year 46.3 (9.68) 48.00 –0.391 27.0–60.0

Weight, kg 76.4 (8.48) 76.00 –0.364 60.0–92.0

Machine’s years of run, year 3.66 (1.52) 4.34 –0.412 1.51–5.50

Average speed, km/h 20.0 (6.37) 20.44 0.940 8.05–52.05

Poor haul road condition (IRI Score) 8.27 (1.44) 8.20 1.367 5.51–14.84

Predictor variables A(8) (m s^–2) VDV(8) (m s^–1.75)

β p-value β p-value

Age, year 0.001 0.938 0.002 0.933

Weight, kg –0.002 0.512 –0.0126 0.317

Years of run, year. –0.020* 0.012 –0.456* 0.012

Average speed, km/h 0.003* 0.026 0.034 0.237

Poor haul road condition, IRI 0.009* 0.002 0.179* 0.006

	Mean (SD)	Median	Skewness	Range	<ISO lower	Within ISO	>ISO upper
Whole body vibration exposure
RMSA
a_wx (anterior-posterior x-axis), ms^–2	0.21 (0.03)	0.21	0.44	0.15–0.30	100.0	0.0	0.0
a_wy (medial-lateral y-axis), ms^-2	0.21 (0.02)	0.21	0.09	0.16–0.27	100.0	0.0	0.0
a_wz (vertical z-axis), ms^–2	0.48 (0.10)	0.47	0.81	0.31–0.81	41.5	58.5	0.0
a_wv (total), ms^–2	0.63 (0.10)	0.62	0.78	0.43–0.97	0.0	96.9	3.1
A(8) (daily exposure), ms^–2	0.48 (0.10)	0.47	0.81	0.31–0.81	41.5	58.5	0.0
VDV exposure
VDV_x (anterior-posterior x-axis), ms^–1.75	2.37 (0.37)	2.36	0.34	1.71–3.36	100.0	0.0	0.0
VDV_y (medial-lateral y-axis), ms^–1.75	2.34 (0.29)	2.36	–0.12	1.63–3.02	100.0	0.0	0.0
VDV_z (vertical z-axis), ms^–1.75	5.24 (1.22)	5.13	1.07	2.92–9.25	96.9	3.1	0.0
VDV_sum (total), ms^–1.75	5.38 (1.18)	5.26	1.09	3.15–9.31	96.9	3.1	0.0
VDV(8) (daily exposure), ms^–1.75	10.90 (2.25)	10.56	1.11	7.32–17.99	9.2	90.8	0.0
Personal and operational factors
Age, year	46.3 (9.68)	48.00	–0.391	27.0–60.0
Weight, kg	76.4 (8.48)	76.00	–0.364	60.0–92.0
Machine’s years of run, year	3.66 (1.52)	4.34	–0.412	1.51–5.50
Average speed, km/h	20.0 (6.37)	20.44	0.940	8.05–52.05
Poor haul road condition (IRI Score)	8.27 (1.44)	8.20	1.367	5.51–14.84
Predictor variables	A(8) (m s^–2)	VDV(8) (m s^–1.75)
	β	p-value	β	p-value
Age, year	0.001	0.938	0.002	0.933
Weight, kg	–0.002	0.512	–0.0126	0.317
Years of run, year.	–0.020*	0.012	–0.456*	0.012
Average speed, km/h	0.003*	0.026	0.034	0.237
Poor haul road condition, IRI	0.009*	0.002	0.179*	0.006

Abbreviations: SD, standard deviation; n, number of subjects, RMSA, frequency-weighted root mean square acceleration; VDV, frequency-weighted vibration dose value; CF, crest factor, ^aISO 2631-1 Health guidance caution zone (HGCZ) (1997) for RMSA: lower limit = 0.45 ms^–2, upper limit = 0.9 ms^–2; for VDV: lower limit = 8.5 ms^–1.75, upper limit = 17 ms^–1.75. All machines were KOMATSU HD-785-7 dump trucks. Statistically significant; *p < 0.05.

Table 2 revealed that 52.3% of the dumper operators had aged more than 10 years in the job and 81.5% of the dumper operators were overweight. The dumper operators were much more affected than the controls for lower back pain (50.8%), upper back pain (29.2%), and knee and leg pain (21.5%). The results also revealed that 49% of the dumper operators were found to be working in a poor posture (RULA score 5–6), which requires action to be taken immediately. Some of the dumper operators were placing their upper arm in the range of 45–90° from vertical, which gives unnecessary stress to the shoulders. In some instances, it was also observed that their shoulders were raised, which further adds to the stress on their shoulders. The useless bend in the wrist was up to more than 15° horizontals. Neck bending in the range of 10–20° from vertical was observed. The trunk was bent 0–20° from the vertical during the entire work period. Neck and trunk twists and side bending were observed during reversing the dumper.

Table 2

Characteristics of dumper operators in iron ore mines (n = 65)

	(n = 65) % or mean (SD)
Musculoskeletal disorders ^b
Neck pain	7.7
Upper limb and shoulder pain	13.8
Shoulder pain	12.3
Upper limb pain	4.6
Upper back pain	29.2
Lower back pain	50.8
Knee and leg pain	21.5
Knee pain	20.0
Leg pain	9.2
Age (yr)
20–29	7.7
30–39	21.5
40–49	27.7
50–60	43.1
Mean (SD)	46.3 (9.7)
More than 10 years with the job	52.3
Height (cm)
<176	90.8
176–181	4.62
>181	4.6
Overweight (body mass index 23 kg/m² or over)	81.5
Mean body mass index (standard deviation)	26.1 (3.5)
RULA score (assessment of workplace posture)
1–2	0
3–4	35.4
5–6	49
7	15.6

Abbreviations: SD, standard deviation; n, number of subjects. ^bbased on the Nordic musculoskeletal questionnaire (NMQ).

Table 3 compared the anthropometric data of operators and seat dimensions. Most operators faced issues with inappropriate seat dimensions, including backrest height, seat pan length, seat pan width, and seat height. Approximately 95% of operators had a sitting height higher than the backrest height, leaving their backs unsupported and potentially causing neck pain. Additionally, around 5% of operators had a buttock popliteal length of 38.60 cm against a seat pan length of 49 cm, resulting in improper lumbar support and restricted knee movement. The seat height of 45 cm left most operators with unsupported feet while driving, potentially leading to long-term musculoskeletal disorders (MSDs). Moreover, many dumpers had hard seats and worn-out cushions, and poor maintenance planning resulted in malfunctioning seat suspensions. These factors, when combined, contribute to MSD problems for operators, especially during prolonged periods of work.

Table 3

Dimensions of seat and anthropometric measurements of dumper operators (n = 65)

Seat components	Seat dimension, cm		Associated anthropometry	Anthropometric measurements, cm
	min	max		min	max	5^th percentile	95^th percentile
Height of Backrest	–	68	Sitting Height (erect)	72.30	94.30	75.56	86.06
Length of the seat pan	–	49	Buttock Popliteal Length	35.50	55.80	38.60	49.32
Width of Seat pan	–	52	Buttock Width	29.30	43.30	29.84	39.90
Height of Seat	37	45	Popliteal Height	39.70	48.50	40.34	46.96

Table 4 revealed that the dumper operators had a high risk of accelerated aging for upper back pain (adjusted odds ratio for age aOR = 3.75 p < 0.05), and lower back pain (aOR = 2.20, p < 0.05), lower back pain (aOR = 2.16, p < 0.01) and for knee and leg pain (aOR = 2.18, p < 0.05) (Table 4). Being overweight was significantly associated with upper back pain only (aOR = 3.52, p < 0.10) (Table 4). Based on the adjusted odds ratio, the WBV exposure in terms of A(8) was found to be significantly associated with upper limb and shoulder pain (aOR = 4.49, 95% CI: 1.10–20.4, p < 0.05), lower back pain (aOR = 5.79, 95% CI: 1.16–28.7, p < 0.05) and “knee and leg pain” (aOR = 4.27, 95% CI: 1.02–17.8, p < 0.05). Similarly, VDV(8) was found to be associated with upper back pain (aOR = 4.15, 95% CI: 1.01–21.2, p < 0.05) and lower back pain (aOR = 4.78, 95% CI: 1.03–21.9, p < 0.05). The poor work postures were significantly associated with musculoskeletal disorders: neck pain (aOR = 4.81, 95% CI: 1.32–17.5, p < 0.05), upper limb and shoulder pain (aOR = 5.09, 95% CI: 1.24–20.8, p < 0.05), and lower back pain (aOR = 3.67, 95% CI: 1.01–13.4, p < 0.05). Also, poor seat design was found to be associated with “knee and leg pain” (aOR = 4.31, 95% CI: 1.00–18.6, p < 0.05).

Table 4

Associations between musculoskeletal disorders ^a and risk factors in dumper operators (n = 65): odds ratio (OR) and 95% confidence interval (95% CI)

Musculoskeletal disorders
	Neck pain		Upper limb and		Upper back pain		Lower back pain		Knee and leg pain
			shoulder pain
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
	Crude odds ratio (cOR) and 95% confidence interval
Age of operators	1.05	(0.63 to 1.74)	0.98	(0.59 to 1.63)	2.46^*	(1.36 to 4.45)	1.58	(0.93 to 2.67)	1.25	(0.74 to 2.10)
Overweight (body mass index 23 kg/m² or over)	0.91	(0.33 to 2.54)	1.56	(0.55 to 4.37)	1.32	(0.47 to 3.66)	1.53	(0.54 to 4.37)	0.71	(0.24 to 2.03)
A(8), frequency-weighted RMS acceleration	1.50	(0.45 to 4.94)	4.12^*	(1.03 to 16.6)	1.62	(0.49 to 5.35)	4.58^*	(1.26 to 16.7)	5.46^*	(1.48 to 20.1)
VDV(8), vibration dose value	0.67	(0.23 to 1.96)	2.36	(0.76 to 7.29)	1.78	(0.61 to 5.27)	2.34	(0.79 to 6.97)	0.60	(0.19 to 1.85)
Poor work posture	3.13 ^*	(1.09 to 8.98)	4.24^*	(1.39 to 12.9)	6.46^*	(2.07 to 20.1)	6.62^*	(2.16 to 20.3)	1.92	(0.69 to 5.39)
Poor seat design	0.88	(0.27 to 2.78)	2.00	(0.59 to 6.69)	4.13^*	(1.15 to 14.8)	5.33^*	(1.47 to 19.3)	4.25^*	(1.24 to 14.4)
	Adjusted odds ratio (aOR) and 95% confidence interval
Age of operators	0.87	(0.50 to 1.52)	1.01	(0.99 to 0.54)	3.75^*	(1.63 to 8.63)	2.20 ^*	(1.05 to 4.55)	1.13	(0.33 to 3.88)
Overweight (body mass index 23 kg/m² or over)	0.84	(0.28 to 2.50)	1.55	(0.48 to 4.98)	3.52†	(0.79 to 15.6)	1.07	(0.27 to 4.11)	0.61	(0.18 to 2.01)
A(8), frequency-weighted RMS acceleration	1.09	(0.28 to 4.24)	4.49^*	(1.10 to 20.4)	1.31	(0.28 to 6.12)	5.79^*	(1.16 to 28.7)	4.27^*	(1.02 to 17.81)
VDV(8), vibration dose value	0.49	(0.14 to 1.68)	2.92	(0.80 to 10.6)	4.15^*	(1.01 to 21.2)	4.78^*	(1.03 to 21.9)	0.73	(0.19 to 2.67)
Poor work posture	4.81 ^*	(1.32 to 17.5)	3.28^*	(1.01 to 11.62)	5.09 ^*	(1.24 to 20.8)	3.67^*	(1.01 to 13.4)	1.13	(0.33 to 3.88)
Poor seat design	0.45	(0.10 to 1.88)	0.84	(0.19 to 3.68)	2.93	(0.61 to 13.9)	3.77	(0.77 to 18.5)	4.31^*	(1.00 to 18.6)

Bold types: significant OR: *p<0.05, †close to significant (p < 0.10). Cases: dumper operators in iron ore mines, controls: mine office workers in the same mines. ^aBased on the Nordic Musculoskeletal Questionnaire (NMQ).

Multiple logistic regression analysis shows that the risk factor of age, WBV exposure, poor posture, and poor seat design were significantly associated with MSD risk. Afterward, these four independent risk factors were decomposed into components with multiple aspects (Table 5). These multiple aspects represent the targets where improvements can be made. Based on survey questionnaire items, ITs were formed, and subsequently, SMs were constructed. Poor seat design and poor posture were decomposed to form 7 and 3 ITs, respectively, while age and WBV exposure each were disintegrated into 2 and 4 components, respectively. The ITs and SMs formulated for this study are shown in Table 5.

Table 5

Enhancement and safety measures hierarchies

Significant factors	Logistic regression coefficients [ln (odds ratio)]	Max [ln (odds ratio)]	Improvement targets (ITs)	Safety Measures (SMs)
Age			Measures to counter age-related MSDs
(aOR_{upper _ back} = 3.75; aOR_{lower _ back} = 2.20)
	β_{upper _ back}=1.322 β_{lower _ back}=0.788	(β=1.322)	IT 1: Motivate workers to engage in physical exercise	SM 1: Promote a culture of healthy living through training, workshops, and seminars
			IT 2: Enhance rest breaks	SM 2: Implement job rotation policy
WBV exposure, A8 and VDV(8) (aOR_{upper _ back _ A8} = 5.79, aOR_{lower _ back _ VDV8} = 4.78; aOR_{upper _ back _ VDV8} = 4.15; aOR_{upper _ limb _ A8} = 4.49; aOR_{knee _ leg _ A8} = 4.27)			Measures to minimize WBV exposure
	β_{upper _ back _ A8} = 1.756 β_{lower _ back _ VDV8} = 1.568 β_{upper _ back _ VDV8} = 1.423 β_{lower _ limb _ A8} = 1.502		IT 3: Strengthen control measures to check for speed limit	SM 3: Establish safe speed zones and install advisory signs at mine sites
	β_{knee _ leg _ A8} = 1.452	(β=1.756)	IT 4: Improve awareness about the speed limit	SM 4: Regularly assess and evaluate speed on the job while incorporating a warning system
			IT 5: Improve maintenance of haul road	SM 5: Conduct frequent evaluations of haul road conditions and take necessary measures
			IT 6: Ensure proper haul road design at the primary stage	SM 6: Avoiding the sharp turns and sudden elevation gain, wherever possible
			Measures to improve the dumper’s seat
			IT 7: Incorporate ergonomic aspects in seat design	SM 7: Design seats according to human body contours and anthropometry using ergonomic process assessment
			IT 8: Improve seat suspension	SM 8: Integrate a mechanized vibration isolation system in the seat suspension
Poor seat design (aOR_neck = 4.31)	β_neck=1.461	(β=1.461)	IT 9: Enhance seat cushion quality	SM 9: Schedule regular maintenance and periodic seat replacements
			IT 10: Increase lumbar comfort	SM 10: Incorporate adjustable lumbar support into seat design
			IT 11: Improve upper leg comfort	SM 11: Use high-quality materials for seat cushioning
			IT 12: Ensure head and upper back comfort	SM 12: Enhance adjustability of the seat backrest and headrest
			IT 13: Simplify seat adjustment process	SM 13: Mechanize all seat controls as per operator requirements
			Measures to enhance work posture
Poor work posture (aOR_neck = 4.81; aOR_{upper _ limb} = 3.28; aOR_{upper - back}=5.09; aOR_{lower _ back} = 3.67)			IT 14: Improve seat adjustability in dumpers	SM 14: Increase the level of adjustability for each adjustable part
	β_neck=1.571 β_{upper _ limb}=1.188	(β=1.627)	IT 15: Ensure job suitability for workers	SM 15: Select a worker suitable for job
	β_{upper _ back}=1.627 β_{lower _ back}=1.300		IT 16: Provide training and awareness on proper seat rest and posture	SM 16: Conduct periodic safety training and refresher courses on the benefits of using lumbar support and headrest

Note: aOR: adjusted odds ratio (see Table 4); β: regression coefficient (see Table 4); IT: Improvement targets Enhancement measures; SM: Safety measures.

Table 6 shows the linguistic label and corresponding fuzzy number used for the experts’ judgment in the questionnaire survey. Table 7 shows the result of an aggregation of fuzzy opinions for selecting a particular safety measure corresponding to an improvement target (IT3→SM3: Table 5). The defuzzified crisp value (weight) of 6.06 was assigned to the SM3. Similarly, all the SMs were assigned the weight (referred to as E_i in Table 8) according to the aggregation of fuzzy judgments from the experts. Table 8 presents the standardized impact rating for ITs and the priority weights of the corresponding SMs. Table 8 also shows the safety measures preferences, which are ordered according to their rank calculated based on the quantification process described in the methodology section. The priority weight of various SMs varied from 3.75 (the least important) to 8.37 (the most important).

Table 6

Linguistic label and corresponding fuzzy number

S.no.	Linguistic label	Fuzzy number
1	Strongly ineffective (SI)	[0,1,2, 0,1,2]
2	Ineffective (I)	[1,2,3 , 1,2,3]
3	Somewhat ineffective (SWI)	[2,3,4 , 2,3,4]
4	Neither effective nor ineffective (N)	[3,4,5 , 3,4,5]
5	Somewhat effective (SWE)	[4,5,6 , 4,5,6]
6	Effective (E)	[5,6,7 , 5,6,7]
7	Strongly effective (SE)	[6,7,8 , 6,7,8]

Table 7

Aggregation of fuzzy opinions for selecting a particular safety measure corresponding to an improvement measure (IT3→SM3)

Q. How to strengthen control measures to check for speed limits in dumpers
Option 1. Penalty if speed limit is reached		Option 2. Establishing safe speed zones and advisory signs at the mine sites		Option 3. Creating a traffic environment as similar to operators encounter in public road network		Option 4. Development of traffic management plan in mines
Expert judgement and corresponding fuzzy number		Expert judgment and corresponding fuzzy number		Expert judgment and corresponding fuzzy number		Expert judgement and corresponding fuzzy number
Expert	Expert opinion	fuzzy number	Expert opinion	Fuzzy number	Expert opinion	Fuzzy number	Expert opinion	Fuzzy number	Relative agreement degree (RAD₁)	Relative agreement degree (RAD₂)	Relative agreement degree (RAD₃)	Relative agreement degree (RAD₄)	Max. Final fuzzy number	Defuzzified crisp value (weight)
E1	SE	[6,7,8 , 6,7,8]	SWE	[4,5,6 , 4,5,6]	SWE	[4,5,6 , 4,5,6]	SWE	[4,5,6 , 4,5,6]	0.127	0.025	0.193	0.108
E2	SWE	[4,5,6 , 4,5,6]	E	[5,6,7 , 5,6,7]	E	[5,6,7 , 5,6,7]	E	[5,6,7 , 5,6,7]	0.027	0.150	0.018	0.145
E3	SWI	[2,3,4 , 2,3,4]	SWI	[2,3,4 , 2,3,4]	SWI	[2,3,4 , 2,3,4]	SWE	[2,3,4 , 2,3,4]	0.064	0.017	0.018	0.102
E4	SI	[1,2,3 , 1,2,3]	N	[3,4,5 , 3,4,5]	SI	[1,2,3 , 1,2,3]	E	[5,6,7 , 5,6,7]	0.000	0.067	0.000	0.151
E5	SWI	[2,3,4 , 2,3,4]	N	[3,4,5 , 3,4,5]	N	[3,4,5 , 3,4,5]	N	[3,4,5 , 3,4,5]	0.064	0.067	0.096	0.006
E6	E	[5,6,7 , 5,6,7]	SE	[6,7,8 , 6,7,8]	SE	[6,7,8 , 6,7,8]	SE	[6,7,8 , 6,7,8]	0.155	0.125	0.009	0.054
E7	SE	[6,7,8 , 6,7,8]	E	[5,6,7 , 5,6,7]	SWE	[4,5,6 , 4,5,6]	SE	[6,7,8 , 6,7,8]	0.136	0.142	0.193	0.054
E8	E	[5,6,7 , 5,6,7]	SE	[6,7,8 , 6,7,8]	SWE	[4,5,6 , 4,5,6]	E	[5,6,7 , 5,6,7]	0.145	0.133	0.193	0.139
E9	SE	[6,7,8 , 6,7,8]	E	[5,6,7 , 5,6,7]	N	[3,4,5 , 3,4,5]	SWE	[2,3,4 , 2,3,4]	0.145	0.133	0.079	0.096
E10	E	[5,6,7 , 5,6,7]	SE	[6,7,8 , 6,7,8]	SWE	[4,5,6 , 4,5,6]	E	[5,6,7 , 5,6,7]	0.136	0.142	0.202	0.145
Expert	RAD_1 ×fuzzy number for option 1		RAD₂×fuzzy number for option 2		RAD₃×fuzzy number for option 3		RAD₄×fuzzy number for option 4		Final fuzzy number (Option 1)	Final fuzzy number (Option 2)	Final fuzzy number (Option 3)	Final fuzzy number (Option 4)	[5.058 , 7.058]	6.06
E1	[0.764, 0.891, 1.018]		[0.100, 0.125, 0.150]		[0.772, 0.965, 1.158]		[0.434, 0.542, 0.651]
E2	[0.109, 0.136, 0.164]		[0.750, 0.900, 1.050]		[0.088, 0.105, 0.123]		[0.723, 0.867, 1.012]
E3	[0.127, 0.191, 0.255]		[0.033, 0.050, 0.067]		[0.035, 0.053, 0.070]		[0.205, 0.307, 0.410]
E4	[0.000, 0.000, 0.000]		[0.200, 0.267, 0.333]		[0.000, 0.000, 0.000]		[0.753, 0.904, 1.054]
E5	[0.127, 0.191, 0.255]		[0.200, 0.267, 0.333]		[0.289, 0.386, 0.482]		[0.018, 0.024, 0.030]		[5 , 7]	[5.058 , 7.058]	[3.825 , 5.825]	[4.392 , 6.392]
E6	[0.773, 0.927, 1.082]		[0.750, 0.875, 1.000]		[0.053, 0.061, 0.070]		[0.325, 0.380, 0.434]
E7	[0.818, 0.955, 1.091]		[0.708, 0.850, 0.992]		[0.772, 0.965, 1.158]		[0.325, 0.380, 0.434]
E8	[0.727, 0.873, 1.018]		[0.800, 0.933, 1.067]		[0.772, 0.965, 1.158]		[0.693, 0.831, 0.970]
E9	[0.873, 1.164]		[0.667, 0.800, 0.933]		[0.237, 0.316, 0.395]		[0.193, 0.289, 0.386]
E10	[0.682, 0.818, 0.955]		[0.850, 0.992, 1.133]		[0.807, 1.009, 1.211]		[0.723, 0.867, 1.012]

Note: RAD: relative agreement degree.

Table 8

Calculation of standardized impact rating of improvement targets and standardized priority weights of safety measures

Enhancement measures (EMs)	Mean score (MS_i)	P_i: probability score: [(MS_i/∑MS_i)×100]	M_i: Impact rating: (P_i×β)	m_i: Standardized impact rating: [(M_i/∑M_i)×100	Safety Measures (SMs)	E_i: Efficiency Score based on Defuzzified crisp value	p_i: Priority score: (m_i×E_i)	P_i: Standardized priority weight: [(p_i/∑p_i)×100]	Priority rank
IT 1	1.69	4.36	5.77	3.72	SM 1	6.58	24.45	3.75	16
IT 2	2.50	6.45	8.53	5.50	SM 2	6.64	36.50	5.59	11
IT 3	2.17	5.60	9.84	6.34	SM 3	6.06	38.41	5.89	9
IT 4	2.29	5.91	10.38	6.69	SM 4	6.38	42.67	6.54	7
IT 5	2.54	6.56	11.52	7.42	SM 5	6.71	49.78	7.63	2
IT 6	2.88	7.44	13.06	8.41	SM 6	6.49	54.60	8.37	1
IT 7	2.32	5.99	8.75	5.64	SM 7	6.96	39.24	6.01	8
IT 8	2.12	5.47	8.00	5.15	SM 8	6.9	35.55	5.45	13
IT 9	2.86	7.38	10.79	6.95	SM 9	6.95	48.31	7.40	3
IT 10	2.22	5.73	8.37	5.40	SM 10	6.56	35.39	5.42	14
IT 11	2.82	7.28	10.64	6.85	SM 11	5.46	37.42	5.73	10
IT 12	2.28	5.89	8.60	5.54	SM 12	6.47	35.85	5.49	12
IT 13	2.80	7.23	10.56	6.80	SM 13	6.74	45.86	7.03	6
IT 14	2.78	7.18	11.68	7.52	SM 14	6.33	47.62	7.30	4
IT 15	2.52	6.51	10.59	6.82	SM 15	6.8	46.38	7.11	5
IT 16	1.94	5.01	8.15	5.25	SM 16	6.58	34.55	5.29	15

Note: MS_i = average score; P_i = probability score; M_i = impact rating; m_i = standardized impact rating; E_i = Efficiency score; p_i = priority score; P_i = normalized priority weight.

4 Discussion

4.1 High daily vibration exposure and health risk among dumper operators

The study findings indicate that the majority of dumper operators (59%) were exposed to daily vibration levels based on A(8) values exceeding the ISO lower limit (0.45 m s^-²). Similarly, almost all operators (91%) had daily vibration dose values, VDV(8), surpassing the ISO lower limit (8.5 m s^-1.75). The mean values of RMSA and VDV were significantly lower in the x- and y-axes compared to the z-axis, which is consistent with previous studies on whole-WBV exposure [57 –59]. The elevated vibration in the vertical axis can be attributed to substandard haul road grading, compounded by potholes, ruts, and bumps. Furthermore, the dumpers in the study were equipped with mechanical seat suspension systems, which are less effective at attenuating vibration compared to pneumatic and semi-active magnetorheological damper systems [60]. Laboratory research by Mayton et al. revealed that the use of pneumatic-based rheonetic technology could potentially improve vibration isolation in heavy vehicles, thereby reducing MSD issues [60].

4.2 High musculoskeletal disorders prevalence among dumper operators

This research revealed a high prevalence of multiple musculoskeletal disorders (MSDs) among dumper operators, specifically affecting various body parts such as the upper and lower back, shoulders, and “knees and legs”. Among these MSDs, lower back pain was the most common, affecting 51% of dumper operators, followed by upper back pain (30%) and “knee and leg” pain (22%). The increased risk of various MSDs among dumper operators was found to be attributed to several risk factors, including the age of operators, working in awkward postures, exposure to WBV, and poor seat design. These findings may help to understand the risk patterns of MSDs among dumper operators, identify the operators most at risk and establish preventive measures in India and many countries worldwide. Our findings align with previous studies conducted in similar contexts [61 –64]. For instance, Urwin et al. [61] reported that construction workers commonly experienced pain in the back, knee/leg, and shoulder areas. Backman [62] found that around 70% of truck drivers suffered from lower and upper back pain. Netterstrom and Juel [63] observed a prevalence of recurrent upper and lower back disorders among 57% of Denmark’s bus drivers. Mirzaei and Mohammadi [64] noted that tractor drivers in Zahedan city primarily experienced back pain (56.8%) and knee/leg pain (29.5%), attributing it to WBV. Patterson et al. [65] found that most bus drivers they studied suffered from upper and lower back pain and expressed dissatisfaction with the seat design. Additionally, our findings align with research conducted on drivers and office workers [66 –68].

It was also observed that the dimensions of the dumper’s seat did not align with the anthropometric body dimensions of the operators. While the seat height was adjustable and ranged from 37 to 45 cm, the backrest of the seat was shorter than the sitting height for 90% of the operators. Consequently, a significant portion of the operators’ backs lacked support, leading to awkward postures and potential MSDs. Furthermore, the seat’s curvature and length exceeded the buttock popliteal length for 90% of the operators, while the seat’s width exceeded the buttock width for most operators. The maximum seat height (45 cm) was also shorter than the popliteal height of 95% of the operators, resulting in an inappropriate sitting posture and an increased risk of upper back, lower back, and “knee and leg” pain. It revealed that ergonomic planning was lacking in the mine. As pre the RULA score, operators were found to work with a medium level of MSD risk. Based on discussions with the mine management and dumper operators, it was noted that operators were not given any specialized training about WBV-related MSD problems and the effect of poor posture.

4.3 Design of safety measures

Safety measures can be developed in response to targets to be improved through a systematic approach. A thorough analysis of the safety issue, considering the risk factors of MSDs, can lead to long-lasting solutions. Prioritizing the safety measures helps allocate resources efficiently. The approach to the implementation of safety measures used in this study has several advantages. Risk factors-based safety measures have a long-term effect and can align with industry management policy changes. This approach included the multiple facets where responsibility for the reduction in MSD risk is shared between operators and mine managers/supervisors. This approach actively implements preventive measures emphasizing their importance and aligns with previous research. Also, the efficient and effective implementation of such safety measures can reduce MSD risk in the long term [69 –73].

In this study, it was found that dumper operators in iron ore mines are exposed to MSD risk, necessitating safety measures to reduce the health effects. Due to a lack of evidence on effective strategies, this study relied on epidemiological studies, literature data, and educational materials. Significant risk factors of MSDs were identified and included in the safety measures. Implementing and prioritizing the effective implementation of measures is crucial to reduce MSDs.

The most important safety measure emphasizes the minimization of sharp turns and abrupt changes in elevation in designing the haul roads whenever feasible (priority rank 1). These sharp turns and elevations on haul roads result in increased exposure to WBV, thereby influencing the risk of MSDs. To effectively mitigate the risk of MSDs associated with the WBV exposure of dumpers on haul roads, it is imperative to conduct frequent evaluations and monitoring of haul road conditions and take necessary measures.

The prioritization of safety measures highlights the importance of scheduled maintenance tasks related to dumpers, as well as the periodic replacement of seat suspension and seat cushions (priority rank 2). Neglecting the proper maintenance of dumpers exposes operators to elevated levels of WBV exposure. Consequently, it becomes imperative for mine management to prioritize the improvement of maintenance practices implemented in the mines to reduce WBV exposure to minimize the MSD risk.

The significance of seat adjustment mechanisms and proper seat design cannot be overstated when it comes to promoting and maintaining optimal posture and alleviating stress on both the upper and lower back regions (priority rank 3 and 4). By incorporating ergonomic principles and designing seats to accommodate the anthropometric characteristics of operators, including the provision of adequate vertical and horizontal travel ranges, the reachability and working posture of operators can be substantially enhanced [74]. The availability of seat adjustment mechanisms allows operators to customize their seating positions according to their specific body dimensions and preferences. These mechanisms enable adjustments in height, tilt, and lumbar support, enabling individuals to find the most suitable and supportive position for their spinal alignment. By facilitating personalized adjustments, seat mechanisms play a crucial role in reducing the risk of musculoskeletal discomfort and injuries caused by prolonged sitting. Vertical travel ranges in seat design refer to the capability of the seat to adjust its height, allowing operators to align their eye level with the intended visual targets or work surfaces. A seat with an appropriate vertical travel range ensures that operators can maintain proper line-of-sight and avoid excessive neck flexion or extension, which can contribute to neck and upper back discomfort [74]. Similarly, horizontal travel ranges in seat design refer to the adjustability of the seat’s position in relation to the controls and workspace. By accommodating individual differences in arms reach and work requirements, seats with adequate horizontal travel ranges allow operators to position themselves optimally, thereby minimizing excessive stretching or straining that can place undue stress on the lower back, shoulders, and arms. This observation is in line with the study conducted by Cebi et al. [74].

The study also highlights the importance of selecting a worker suitable for the job in reducing the risk of MSD. It requires careful consideration of the physical capabilities and limitations of operators, their experience and training, as well as their understanding of ergonomic principles. An understanding of ergonomic principles is an essential quality in a dumper operator. They should be knowledgeable about the importance of maintaining proper body posture and positioning while operating the dumper. This includes factors such as maintaining a neutral spine, avoiding excessive reaching, or twisting, and utilizing seat and control adjustments to support optimal posture. Operators who have a clear understanding of these principles are better equipped to make informed decisions and adapt their techniques to reduce the risk of MSDs.

A range of intermediary interventions including the integration of mechanized vibration isolation systems, regularly assess and evaluate speed of dumpers on the job while incorporating a warning system, and establish safe speed zones and install advisory signs at mine sites can be implemented to reduce the WBV exposure to minimize the adverse health effects of MSD risk. Mechanized vibration isolation systems and reducing the speed of dumpers while driving play a pivotal role as they effectively dampen and minimize the transmission of vibration from the vehicle to the operator, thereby significantly reducing the impact of WBV on the body. Also, job-specific and refresher training programs are instrumental in equipping operators with the necessary knowledge and skills to effectively manage WBV exposure and maintain proper working postures [75 –80]. These training initiatives educate operators on ergonomic principles, safe operating techniques, and the correct utilization of vibration control mechanisms. By continually reinforcing these skills through refresher training, operators remain well-informed and equipped to implement best practices, reducing the potential for WBV-related injuries and MSDs.

The development and implementation of these safety measures are necessary to reduce the MSD risk and thereby improve operator safety and health.

5 Conclusion

This study provides insights into the prevalent MSDs among dumper operators in iron ore mines. This study showed that risk factors such as age and weight of operators, WBV exposure, awkward posture, and poor seat design influence the prevalence of MSDs. The vibration study revealed that dumper operators in mines are at a moderate risk level. The posture study highlights the mismatch between the operators’ body dimensions and seat dimensions. The RULA assessment for dynamic posture reveals that dumper operators frequently assume awkward postures while operating the machinery, increasing their vulnerability to MSD risks.

To address these issues, safety measures were formulated which suggested that equipment manufacturers should give significant consideration to anthropometry and ergonomic seat design. It is recommended that seat suspension systems be incorporated into all dumpers used in mines. The seat backrest height should exceed the erect seating height based on population anthropometry by a few centimetres. The seat pan length should be adjustable to accommodate the requirements of all operators. Seat height should be adjustable in the vertical (z) direction to allow each operator to adjust it according to their needs. Rear-view mirrors should be used for rear viewing instead of frequently peeping out of the window. Proper support for the feet on the cabin floor while driving should be ensured.

Seat design, awkward posture, average vehicle speed, and haul road conditions significantly contribute to WBV exposure. Proper machine design is necessary to mitigate WBV exposure. During the planning stage of acquiring new equipment, operators’ anthropometric data should be considered for ergonomic seat design. The mine administration should take appropriate measures to raise awareness among dumper operators about the health effects of vibration exposure and improve their physical fitness to reduce the risk of MSDs.

The proposed methodology, which involves brainstorming and incorporating domain experts’ opinions to prioritize safety interventions, can be employed to select the most effective safety measure and assist mine management in reducing workers’ MSD risk. Exploring the applicability of our approach in improving worker safety in other mines would be of great interest.

5.1 Strengths and limitations

The present study is an original investigation, which shows that the dumper operators are highly exposed to both the WBV. By investigating the MSDs for various body parts, it was found that the dumper operators were more exposed to multiple MSDs which were strongly associated with risk factors mainly age, WBV exposure, poor posture, poor seat design, and being overweight. Furthermore, this study is one of a kind, which suggested a framework for selecting and prioritizing safety interventions. Although results could not be generalized to other working conditions, the methodology presented could be widely used to identify and prioritize the potential interventions to reduce their effects on workers’ health.

This study has some limitations. It was cross-sectional and conducted in a limited geographic area. The sample was relatively small like many studies in the research area. However, the most significant odds ratio for WBV exposure had a high magnitude with p < 0.05, while it is hard to obtain a significant odds ratio with small samples. Our study was based on widely used self-reported data. The MSDs might be susceptible to recall bias, but these disorders were generally persistent until the time of survey, especially as the work conditions may not change. It may be noted that the MSDs were confirmed by the medical records available at the mine’s hospital.

Our study introduces a fuzzy-based intervention approach, offering a nuanced way to integrate expert judgments but also presents complexities in implementation that are not extensively explored. Moreover, the study’s focus on dumpers from a single manufacturer overlooks potential variations in equipment condition, maintenance, and age, which could affect whole-body vibration exposure and operator’s comfort.

The models and characteristics of machines may play a role in WBV exposure. So, our results were not influenced by them because there was one model only. However, our approach can be performed to compare various models. Our results could not be generalized whereas work conditions are rather similar in open-cast mines in various countries. Hence our findings need to be confirmed by other studies.

Finally, our study shows that fuzzy-based approach for intervention may be useful, but it may appear to be rather complex. However, this challenge can be easily overcome by furnishing user-friendly software tools tailored for implementation in on-field operations. Also, in such cases, training programs, workshops, or educational resources can be provided to field officials, managers, and practitioners to equip them with the necessary understanding associated with fuzzy-based systems, ultimately fostering successful implementation and utilization of safety measures.

Ethical approval

Participation in the study was conducted anonymously, with a focus on respecting the privacy of all adult participants. No intrusive tactics or deception were employed, ensuring a transparent and ethical research approach. Detailed procedures are outlined in the methods section for clarity and transparency. The study protocol was approved by the Department of Mining Engineering of the Indian Institute of Technology, Kharagpur (no. IIT/SRIC/MIN/VIMT/2016-17/74).

Informed consent

The data has been collected anonymously. The participants provided consent to the use of the anonymized data for scientific purposes. A detailed explanation of the procedure is given in the methods section.

Conflict of interest

The authors have no conflict of interest to report.

Footnotes

Acknowledgments

The authors wish to acknowledge the support received from the management, staff, and workers of the case study mines.

Funding

This research was funded by the Sponsored Research and Industrial Consultancy Indian Institute of Technology Kharagpur (SRIC IIT KGP) under project IIT/SRIC/MIN/VIMT/2016-17/74.

References

Skröder

, Pettersson

, Norlén

, Gustavsson

, Rylander

, Albin

, et al. Occupational exposure to whole body vibrations andbirth outcomes –A nationwide cohort study of Swedish women. Sci Total Environ. 2021;751:141476.

Chaudhary

, Palei

, Kumar

, Karmakar

. Whole-body vibration exposure of HEMM operators in surface coal mines: a comparative assessment of transport and non-transport earthmoving equipment operators. Int J Occup Saf Ergon. 2022;28(1):174–83.

Atal

, Palei

, Chaudhary

, Kumar

, Karmakar

. Occupational exposure of dumper operators to whole-body vibration in opencast coal mines –an approach for risk assessment using Bayesian network. Int J Occup Saf Ergon. 2022;28(2):758–65.

Dube

, Chiluba

. Ergonomic factors associated with lower back pain amongst load-haul-dump truck operators at Freda Rebecca gold mine, Bindura, Zimbabwe. J Prev Rehabil Med. 2021;3(1):50–9.

Ijaz

, Ahmad

, Akram

, Thygerson

, Nadeem

, Khan

. Cross-sectional survey of musculoskeletal disorders in workers practicing traditional methods of underground coal mining. Int J Environ Res Public Health. 2020;17(7):2566.

Smith

, Balogun

, Yu

, Jaime

. Health, physical activity and musculoskeletal symptoms among stone, sand, and gravel mine workers: implications for enhancing and sustaining worker health and safety. Safety. 2020;6(4):52.

, Yang

, Sun

, Xue

, Ma

, Liu

. Associations of musculoskeletal disorders with occupational stress and mental health among coal miners in Xinjiang, China: a cross-sectional study. BMC Public Health. 2021;21(1):1–10.

Orelaja

, Wang

, Ibrahim

, Sharif

. Evaluation of health risk level of hand-arm and whole-body vibrations on the technical operators and equipment in a tobacco producing company in Nigeria. J Healthc Eng. 2019;5723830.

Hedge Alan [Homepage on the Internet]; Driving and Back care; spine universe:AStep-by-Step Guide.Available from: Ergonomics Plus, http://ergo-plus.com/.

10.

Kia

, Stephanie

, Sean

, Jeong

. Effect of whole-body vibration exposures on physiological stresses: Mining heavy equipment applications, Appl. Ergon. 2020;85:103065.

11.

Saad

A F

, Askar

. Literature review of musculoskeletal disorders and their risk factors among supermarket cashiers. Rehabil Sci. 2021;6(2):25–40.

12.

Tesfaye

, Mekonnen

, Alemayehu

. Prevalence and risk factors of work-related upper extremity disorders among university teaching staff in Ethiopia, an institution-based cross-sectional study. Pain Res Manag. 2022;744879.

13.

Palikhe

, Yirong

, Choi

. Analysis of musculoskeletal disorders and muscle stresses on construction workers’ awkward postures using simulation. Sustainability. 2020;12(14):5693.

14.

Yong

, Li

, Ge

. A cross-sectional epidemiological survey of work-related musculoskeletal disorders and analysis of its influencing factors among coal mine workers in Xinjiang. Biomed Res Int. 2020;316405.

15.

Ziam

, Laroche

, Lakhal

, Alderson

, Gagne

. Application of MSD prevention practices by nurses working in healthcare settings. Int J Ind Ergon. 2020;77:102959.

16.

Bureau of Labor Statistics. Nonfatal occupational injuries and illnesses requiring days away from work, 2017. Bureau of Labor: Statistics. U.S. Department of Labor; 2018.

17.

Burström

, Aminoff

, Björ

, Mänttäri

, Nilsson

, Pettersson

, Rintamäki

, Rödin

, Shilov

, Talykova

, Vaktskjold

, Wahlström

. Musculoskeletal symptoms, and exposure to whole-body vibration among open-pit mine workers in the Arctic. Int J Occup Med Environ Health. 2017;30(4):553–64.

18.

Bovenzi

, Schust

. A prospective cohort study of low-back outcomesand alternative measures of cumulative external and internalvibration load on the lumbar spine of professional drivers. Scand JWork Environ Health. 2021;47(4):277–86.

19.

Skandfer

, Talykova

, Brenn

, Nilsson

, Vaktskjold

. Low back pain among mineworkers in relation to driving, cold environment and ergonomics. Ergonomics. 2014;57(10):1541–8.

20.

Safiri

, Kolahi

, Cross

. Prevalence, deaths, and disability-adjusted life years due to musculoskeletal disorders for 195 countries and territories 1990-2017. Arthritis Rheumatol2021;73(4):702–14.

21.

Alonso

, Sarría

The economic and social burden of low back pain in Spain: a national assessment of the economic and social impact of low back pain in Spain. Spine. 2020;45(16):1026–32.

22.

Jia

, Zhang

. Prevalence and risk factors analysis for low back pain among occupational groups in key industries of China. BMC Public Health. 2022;22(1):1493.

23.

Donati

, Schust

, Szopa

, et al. Workplace exposure to vibration in Europe: An Expert review. In European Agency for Safety and Health at Work, European Risk Observatory Report EN7:2008; 1-100. Bilbao, Spain: European Agency for Safety and Health at Work.

24.

Griffin

. Handbook of Human Vibration. London: Academic Press; 2012.

25.

Chaffin

, Andersson

, Martin

. Occupational Biomechanics. New York: Wiley; 2006.

26.

Csuhai

ÉA

, Nagy

, Váradi

, Veres-Balajti

. FunctionalAnalysis of the Spine with the Idiag Spinal Mouse System amongSedentary Workers Affected by Non-Specific Low Back Pain. Int. J.Environ. Res. Public Health. 2020;17(24):9259.

27.

Morgan

, Mansfield

. A Survey of expert opinion on the effects of occupational exposures to trunk rotation and whole-body vibration. Ergonomics. 2014;57(4):563–57.

28.

Mansfield

, Griffin

. Effects of posture and vibration magnitude on apparent mass and pelvis rotation during exposure to whole-body vertical vibration. J Sound Vib. 2002;253(1):93–107.

29.

Rahimdel

, Mirzaei

, Sattarvand

. Vibrational health risk assessment for truck operators in mining using artificial neural network. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2022;236(13):2991–3004.

30.

de la Hoz-Torres

, Aguilar

, Martínez-Aires

, Ruiz

. A methodology for assessment of long-term exposure to whole-body vibrations in vehicle drivers to propose preventive safety measures. J Safety Res. 2021;78:47–58.

31.

Stahl

, Domnez

, Jamieson

. A field study of haul truck operation in open pit mines, proceedings of the human factors and ergonomics society 55^th Annual meeting, 2011.

32.

Bovenzi

, Schust

, Mauro

. An overview of low back pain and occupational exposures to whole-body vibration and mechanical shocks. Medicina del Lavoro. 2017;108:419–433.

33.

Tiemessen

, Hulshof

, Frings-Dresen

. Two-way assessment of other physical work demands while measuring the whole-body vibration magnitude. J Sound Vib. 2008;310(4):1080–1092.

34.

Mayton

, Jobes

, Gallagher

. Assessment of whole-body vibration exposures and influencing factors for quarry haul truck drivers and loader operators. Int J Heavy Veh Syst. 2014;21(3):241–61.

35.

Wolfgang

, Limerick

. Whole-body vibration exposure of haul truck drivers at a surface coal mine. App Ergon. 2014;45(6):1700–4.

36.

Alkilani

, Jupp

, Sawhney

. Issues of construction health and safety in developing countries: a case of Jordan. Constr Econ Build. 2013;13(3):141–56.

37.

Soltani

, Mostafa Mirzaei Aliabadi . Risk assessment of firefighting job using hybrid SWARA-ARAS methods in fuzzy environment. Heliyon. 2023;9(11):e22230–0.

38.

Bentley

, Onnis

, Vassiley

, Farr-Wharton

, Caponecchia

, Andrew

, et al. A systematic review of literature on occupational health and safety interventions for older workers. Ergonomics. 2023;1–27.

39.

de Souza

, Rosa

, Patriarca

, Haddad

. Soft computing for nonlinear risk assessment of complex socio-technical systems. Expert Systems with Applications. 2022;206(C):117828.

40.

ISO: Standard 2631-1. Mechanical vibration and Shock–Evaluation of Human Exposure to Whole-Body Vibration. Part 1: General Requirements. International Organization for Standardization, Geneva, 1997.

41.

ISO: Standard 2631-1. Mechanical Vibration and Shock–Evaluation of Human Exposure to Whole-Body Vibration. Part 1: General Requirements. International Organization for Standardization, Geneva, 2010.

42.

ISO: Standard 10326-1. Mechanical vibration –Laboratory method for evaluating vehicle seat vibration. Part 1: Basic requirements. International Organization for Standardization, Geneva, 2016.

43.

ISO: Standard 8041-1. Human response to vibration-Measuring instrumentation-Part1: General purpose vibration meters. International Organization for Standardization, Geneva, 2017.

44.

Ahlin

, Granlund

. Relating Road roughness and vehicle speeds to human whole-body vibration and exposure limits. Int J Pavement Eng. 2002;3(4):207–16.

45.

Hinz

, Seidel

, Menzel

, et al. Effects related to random whole-body vibration and posture on a suspended seat with and without backrest. J Sound Vib. 2002;253(1):265–82.

46.

Global source of software and instrumentation for ergonomics, biomechanics and medicine [Internet]. Quebec: NexGen Ergonomics Inc.; c1997-2020. 2015 Jul 15 [cited 2023 December 13]. Available from: http://www.nexgenergo.com/ergonomics/ergomast.html.

47.

Chowdury Rehman

. Study and analysis of work posture of workers working in a ceramic industry through rapid upper limb assessment (RULA), Int J Engg App Sci2014;5.

48.

Kuorinka

, Jonsson

, Kilbom

, Viterberg

, Bierning-Sorensen

, Andersson

, Jorgensen

. Standardised Nordic Questionnaires for the analysis of musculoskeletal symptom. Appl Ergon. 1987;18(3):233–7.

49.

Haslegrave

, Corlett

Evaluating work conditions and risk of injury- techniques for field surveys. Evaluation of Human Work. 2nd ed. Bristol (PA): Taylor and Francis; 1990.

50.

Aerts

BRJ

, Kuijer

PPFM

, Beumer

, Eygendaal

, Frings-Dresen

MHW

. Development of a novel Work-Related Questionnaire for Upper extremity disorders (WORQ-UP). Int Arch Occup Environ Health. 2017;90(8):823–33.

51.

Yona

, Weisman

, Ingel

, Masharawi

. The cross-cultural adaptation and reliability of the online Hebrew version of the extended Nordic Musculoskeletal Questionnaire. Musculoskelet Sci Pract. 2020;50:102252.

52.

Gomez-Rodríguez

, Díaz-Pulido

, Gutierrez-Ortega

, S'anchez-S'anchez

, Torres Lacomba

. Cultural adaptation, and psychometric validation of the standardised nordic questionnaire Spanish version in musicians. Int. J. Environ. Res. Publ. Health. 2020;17(2):653.

53.

Klir

, Yuan

. Fuzzy sets and fuzzy logic: theory and applications. Possibility theory versus probab. Theory. 1996;32(2):207–8.

54.

de Souza

, Rosa

, Patriarca

, Haddad

. Soft computing for nonlinear risk assessment of complex socio-technical systems. Expert Systems with Applications. 2022;206(1):117828.

55.

Hsu

, Chen

. Aggregation of fuzzy opinions under group decision making. Fuzzy Sets and Systems. 1996;79:279–85.

56.

de Souza

, Rosa

, Vidal

MCR

, Najjar

, Hammad

AWA

, Haddad

. Information technologies in complex socio-technical systems based on functional variability: A case study on HVAC maintenance work orders. Applied Sciences. 2021;11(3):1049.

57.

Johanning

, Fischer

, Christ

, Gores

, Landsbergis

. Whole-body vibration exposure study in US railroad locomotives Dan ergonomic risk assessment. AIHA J. 2002;63(4):439–46.

58.

Salmoni

, Cann

, Gillin

. Exposure to whole-body vibration and seat transmissibility in a large sample of earth scrapers. Work. 2010;35(1):63–75.

59.

Mayton

, Jobes

, Miller

. Comparison of wholebody vibration exposures on older and newer haulage trucks at an aggregate stone quarry operation. In: Proceedings of the ASME Design Engineering Technical Conference & Computers and Information in Engineering Conference 2008. Available from: http://www.cdc.gov/niosh/mining/UserFiles/works/pdfs/cowbv.pdf.

60.

Mayton

, Ducarme

, Jobes

, Matty

. Laboratory testing of seat suspension performance during vibration testing. National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory 2006. ASME 2006 International Mechanical Engineering Congress and Exposition-IMECE-Chicago, Illinois.

61.

Yosef

, Belachew

, Tefera

. Magnitude and Contributing Factors of Low Back Pain among Long Distance Truck Drivers at Modjo Dry Port, Ethiopia: A Cross-Sectional Study. J. Environ. Public Health. 2019;1–7.

62.

van Vreden

, Xia

, Collie

, Pritchard

, Newnam

, Lubman

, et al. The physical and mental health of Australian truck drivers: a national cross-sectional study. BMC Public Health. 2022;22(1).

63.

Mendes

HGO

, de Moraes Tomaz

, Coelho-Oliveira

, de Freitas

, Cristina Moura-Fernandes

, de Souza-Gama

, et al. The Consequences of Mechanical Vibration Exposure on the Lower Back of Bus Drivers: A Systematic Review. Applied Sciences. 2021;11(21):9986.

64.

Joseph

, Vasanthan

, Standen

, Kuisma

, Paungmali

, Pirunsan

, et al.Causal Relationship Between the Risk Factors and Work-Related Musculoskeletal Disorders Among Professional Drivers: A Systematic Review. Hum. Factors J. Hum. Factors Ergon. Soc. 2021 Jun 14;001872082110065.

65.

Yitayal

, Ayhualem

, Fiseha

, Kahasay

, Gashaw

, Gebre

. Occupational lower back pain and associated factors among taxi drivers in Mekelle city, north Ethiopia: a cross-sectional study. Int. J. OccuSaf. Ergon. 2021;1–6.

66.

Luiz Felipe Ferreira-Souza , Antonio

, Ana Carolina Coelho-Oliveira , Cunha

, Bernardo-Filho

. Could Whole-Body Vibration Lead the Professional Drivers to Fatigue? Narrat. Rev. J. Ergon. 2021;11(1):1–5.

67.

Verma

, Tiwari

, Verma

, Sai Bharath

, Tomar

. Occupational lower back pain among bus conductors of Udupi district, Karnataka. Int. J. OccuSaf. Ergon. 2020;1–5.

68.

Nabi

, Hawlader

MDH

, Naz

, et al. Low back pain among professional bus drivers: a cross-sectional study from Bangladesh. BMC Public Health. 2023;23:1172.

69.

Babur

, Cevikcan

, Durmusoglu

. Axiomatic design for lean-oriented occupational health and safety systems: an application in shipbuilding industry. Comput Ind Eng. 2016;100:88–109.

70.

Sadeghi

, Dantan

, Siadat

, Marsot

. Design for human safety in manufacturing systems: applications of design theories, methodologies, tools, and techniques. J Eng Des. 2016;27(12):844–77.

71.

Sadeghi

, Houshmand

, Valilai

. Applications of axiomatic design theory in design for human safety in manufacturing systems: A Literature Review. MATEC Web of Conferences. 2017;127:1020.

72.

Pilbeam

, Karanikas

, Steinmann

, Baker

, Khan

. Designing safety interventions for specific contexts: Full Report, 2022.

73.

Verma

, Maiti

, Boustras

. Analysis of categorical incident data and design for safety interventions using axiomatic design framework. Safety Science. 2020;123:104557.

74.

Cebi

, Kahraman

. Determining design characteristics of automobile seats based on fuzzy axiomatic design principles. Int J Comput Intell Sys. 2010;3(1):43–55.

75.

Realyvásquez-Vargas

, Arredondo-Soto

, Blanco-Fernandez

, Sandoval-Quintanilla

, Jiménez-Macías

, García-Alcaraz

. Work Standardization and Anthropometric Workstation Design as an Integrated Approach to Sustainable Workplaces in the Manufacturing Industry. Sustainability. 2020;12(9):3728.

76.

Zhao

, Zhu

, Zhou

, Song

, Zhang

. An axiomatic design theory for design of apparel products. J. Eng. Fibers Fabr. 2022;17:155892502211343–155892502211343.

77.

Zhang

, Chen

, Zhao

, Nie

. Research on Decision Making of Design for Coupling System Based on Complexity Composite. Processes. 2022;10(3):542.

78.

Zhou

, Duan

. Interaction force modeling and analysis of the human–machine kinematic chain based on the human–machine deviation. Sci Rep. 2023;13:17393.

79.

Cohen

, Colligan

. Assessing occupational safety and health training. Full Report, 1998 [Internet]. Centre for Disease Control and Prevention, NIOSH, Cincinnati; Ohio. Available from: https://www.cdc.gov.

80.

Barati

, Ghorbani

, Khalid

, et al. Impact assessment of e-trainings in occupational safety and health: a literature review. BMC Public Health. 2023;23:1187.