Building Services Engineering Research & Technology

In the residential building sector, the use of floating floors is a common practice which increasingly used to reduce vibrations and impact noise. These are usually made from industrial materials, although the emerging concern for sustainable construction is leading to the use of other materials from recycled waste. This article studies the performance of rubber, cork, and cigarette butts as a floating floor. For this purpose, their acoustic properties (ISO 9052-1 and 12,354-2 standards) are analyzed and compared with those of some commercial materials. The results obtained indicated that the performance of these eco-materials is equal or superior to that of commercially available materials.

The number of building occupants is an important indicator for predicting building energy consumption and developing control strategies for building automation. However, most occupancy estimation models were developed depending on the training steps where the true number of occupants is necessary. In order to calculate the occupant number independently, the newly-developed parameter estimation models were proposed, which are based on Maximum Likelihood (ML) approach and Bayesian analysis. A combination of multiple common measurements is used, including real-time CO₂ concentration, energy consumption of facilities and make-up air system. The model starts by smoothing the raw CO₂ concentration data using moving average, two-hour median as well as globally smooth. Then, ML and Bayesian analysis are used to establish the occupancy estimation models. The proposed models are evaluated in a commercial office which contains 36 occupants for validation. We find that the calculation errors could be reduced by using moving averaged data and globally smoothed data. The superiority of the parameter estimation models can be identified based on its lower calculation error and higher calculation accuracy compared to the previous established models.

The traditional elevator system design practice is to calculate the round trip time (RTT) and associated parameters of pure incoming traffic during up-peak, followed by real-time computer simulation. Recent studies indicated that the normal traffic is much more complicated, consisting of a mixture of incoming, outgoing and interfloor patterns. A major breakthrough to analytically calculate the Universal RTT, under such complicated traffic patterns, emerged 6 years ago based on an appropriate origin-destination matrix describing the passenger transit probability. That genesis model played safe by assuming that the total number of passengers demanding service within one round trip is limited elevator contract capacity, which is in line with the traditional up-peak incoming RTT formulae. In this article, such assumption is removed and the study is based on Monte Carlo simulation. It is found that there is room for enhancing the handling capacity, up to two times the contract capacity, by not sacrificing the RTT and average passenger transit time by too much. This phenomenon, that is, total passenger demand beyond contract capacity, is only valid under the existence of multiple entrance floors and/or mixed traffic conditions. This approach may prevent oversizing the design which could be more realistic.

Practical applications: Elevator system designers, according to ISO 8100:32:2020 and CIBSE Guide D: 2020, are recommended to carry out calculation of the RTT and related parameters before any real-time computer simulation. This practice has been adopted by the elevator industry for decades. However, conventional RTT evaluation is mainly on pure incoming traffic during up-peak. The Universal RTT calculation method developed in 2014–15 extended RTT evaluation to cover dominant and complicated traffic patterns of modern buildings, but the assumed number of passengers to be handled within one round trip was limited to the contract capacity of the elevator. This article further removes this limitation to evaluate the limit of handling capacity with reasonable RTT and average passenger transit time. Then, the Universal RTT method could be more realistic and rolled out, and prevent oversizing the system design.

Buildings’ environmental conditions were changed drastically around the world due to the COVID-19 pandemic hazards and restrictions. New social distance rules and organizational changes in the buildings appeared to require a modified fire safety evacuation analysis. The total number of building users under the revised requirements was often limited. Some additional restrictions, such as the reduction of evacuation exit availability, could cause escape problems in the case of fire. In order to determine how the pandemic restrictions could influence the evacuation conditions, a sports hall building was used to assess the impact of the restrictions on evacuation strategies. The research covered test evacuation simulations using the ‘Pathfinder’ modelling software, as well as manual calculations of the expected evacuation time. It was found that the pandemic social distance requirements could cause adverse evacuation conditions in the case of fire. The research helped formulate a simple mathematical algorithm for determining safety evacuation parameters under pandemic restrictions.

The development of the IoT is rapidly growing. These IoT devices are mainly deployed to control and report environmental changes, prevent risks, and bring many beneficial services. However, these benefits may open doors to the adversaries in conjunction with security vulnerabilities and privacy issues. In the recent past, Blockchain has been an emerging technology that reaches several use-cases apart from cryptocurrency. For instance, IoT integration with Blockchain implementation yet indefinite required further research because of resource-constrained IoT devices and ledger-based Blockchain protocol design. This paper presents the systematic implementation of securing IoT devices by enabling the Ethereum Blockchain smart contract. The results show that the collected information is securely stored in the Blockchain after successful authentication. Practical Application: Blockchain innovations have the power to transform manufacturing, construction, healthcare and building supply chains by eliminating the middleman, streamlining operations, improving overall security, and simplifying data management. Onboarding, recordkeeping, client screening, data management, security, privacy, and transaction and trade processing are examples of several practice applications in the financial, insurance, and eHealth services industries. Thus, this study ensures security by enabling Ethereum blockchain and smart contracts in an authentic blockchain applications for building sustainable environments to improve readability and trustworthiness of the transactions.

Design coordination and clash detection are the most common and appreciated applications of three-dimensional modeling (3D modeling). In some projects, millions of clashes are detected including a large number of irrelevant clashes. The purpose of this research is to determine the priority of resolving clashes before the construction phase. In this research, the results of Autodesk Navisworks have been used to improve the process of clash detection. Also, this study attempts to use the fuzzy-AHP for weighting criteria and then, by presenting a relationship, to provide a basis to prioritizing clashes for their resolution and, finally, for identifying irrelevant clashes. This method has been tested on a real project, and the comparison of the expert opinions and the proposed method showed that applying the proposed relationship can identify important and irrelevant clashes.

Stochastic models for estimating residential water demand use high-resolution field data consuming large costs and significant time. An attempt for the accurate estimation of water demand may result in its complex analytical model due to numerous factors affecting the water use event. Moreover, as the water supply system is always subjected to variations in demand, the accuracy of water demand estimation in its design can be side-lined. The water demand in residential buildings is mainly governed by the users’ characteristics and their daily schedule. In this view, the use of Fuzzy Logic can be advantageous to model the uncertainty in water demands. The presented study attempts to provide a methodology to estimate urban indoor residential water demand with the help of user-based end-use models in the absence of field data and generate various possible water demand patterns of fixtures. Usergroups were created for assuming spatial variations in water demand. Fuzzy Logic was used to develop the end-use models using data on urban users’ characteristics, their diurnal activities, and water use habits to estimate the demand characteristics of fixtures. The model may also facilitate the computation of pipe sizing in building water supply systems.

Data-driven building operation and maintenance research such as metadata inference, fault detection and diagnosis, occupant-centric controls (OCCs), and non-invasive load monitoring have emerged (NILM) as independent domains of study. However, there are strong dependencies between these domains; for example, quality of metadata affects the usability of fault detection and diagnostics techniques. Further, faults in controls hardware and programs limit the performance of OCCs. To this end, a literature review was conducted to identify the dependencies between these domains of research. Additionally, real-world examples using operational data from three institutional buildings in Ottawa, Canada, were provided and discussed to demonstrate these dependencies. Finally, a holistic tool-agnostic workflow was introduced which suggested the implementation of operational energy efficiency measures in the following order to ensure their full potential: (1) improve metadata, (2) address faults, (3) implement OCCs, and (4) monitor enhanced key performance indicators (KPIs). The proposed workflow is intended to be comprehensive, reproducible, nonintrusive, and inexpensive to implement.

Practical applications: Optimization of building operations has been emerging among energy management professionals as a relatively low-cost means to achieve energy efficiency and minimize occupants’ discomfort. To this end, this study introduces a tool-agnostic data-driven workflow to building energy management practitioners that can assist them in achieving increased energy efficiency. The proposed workflow recognizes the interdependency of the various domains of research which have historically been treated independently.