Mapping Unmanned Aerial System Data onto Building Information Modeling Parameters for Highway Construction Progress Monitoring

Abstract

The transportation infrastructure management sector lacks automated procedures that can help it find and resolve the performance deviations. The objective of this research is to illustrate the mapping of Unmanned Aerial System (UAS) collected photogrammetric data to building information modeling (BIM) parameters, and their application for automated construction progress monitoring and the generation of as-built models. The goal is to support project managers to estimate project progress during highway construction. As a part of ongoing work, this paper takes into account 4D (3D + time) data that is acquired from 3D surface digital elevation models, point clouds, LiDAR data, and orthographic photos. It maps these 4D data onto BIM parameters to create as-built models of the project at different intervals. A comparison between as-planned and as-built models using the earned value management method is employed to develop metrics that can be used for indicating cost and schedule deviations during construction. The mapping methodology introduced in this paper is illustrated using an ongoing highway construction project case study. The main contribution of this paper is the organization, processing, and integration of UAS data with BIM data structures and project management workflows. The research outcomes will assist project managers in an easy and quick identification of potential performance problems and support the project management decision-making process.

Keywords

data and data science information systems and technology building information modeling (BIM)construction information management remote sensing visualization in transportation 3D-4D-5D modeling data visualization infrastructure construction concrete pavement construction and rehabilitation nondestructive construction management general inspection modeling scheduling infrastructure management and system preservation pavement management systems decision-making: pavement management pavement: asset management

Although time and cost deviations in a construction project are preventable, it is not easy for project managers to detect progress deviations during the construction phase ( 1 ). Local governments and transportation agencies require fast and cost-effective tools to collect and process construction information into meaningful metrics that can support effective management of project progress ( 2 ). A major challenge is the time- and resource-intensive nature of daily manual data collection and data extraction from construction documents. This can distract project managers from the important task of decision-making ( 3 ). Use of remote sensing technologies, and, in particular, Unmanned Aerial Systems (UAS), offers a solution that can collect volumes of visual data and process them in meaningful ways that can support the ease of project decision-making. The objective of this research is to illustrate workflows that can be used to process and automate UAS data collected for construction progress monitoring and 3D model generation of constructed assets, by integrating them with Building Information Modeling (BIM) parameters. The goal is to support project managers to estimate project progress during highway construction.

Remote sensing is defined as “any surveying method which does not require physical contact with the road surface or subsurface” ( 4 ). Remote sensing techniques have the ability to capture 3D data with high spatial and temporal resolutions and provide an efficient approach for inspecting and managing transportation assets. Such techniques can be implemented using various platforms, including moving vehicles, UAS, airplanes, and satellites. There has been a significant increase in remote digital image and video capturing, discography, in the architecture, engineering, construction, and facility management (AEC/FM) industry over the past years ( 3 ).

An unmanned aerial vehicle (UAV) is an aircraft without a human pilot on board. A system consisting of a UAV, a ground-based controller, and a communication system between these is called a UAS. Cameras and sensors are mountable on a UAS to capture different types of data. These sensors include but are not limited to thermal, light detection and ranging (LiDAR), ground penetrating radar (GPR), lightweight portable radiometer (LPR), optical (infrared), ultrasonic (US) sensors, and tetracam multispectral cameras. These sensors are utilized to collect data in different formats, such as digital (images/video), LiDAR, and thermal data. They can be processed to create information such as distances, areas, elevations, angles, volumes, object sizes, and object shapes ( 5 ). The potential advantages of UAS are: (a) improved data acquisition, (b) reduced cost, (c) moving at higher speeds than ground vehicles, (d) real-time data collection, (e) safety improvements, (f) wide view of a whole network being controlled off-line, and (g) flying in very poor conditions or very bad weather ( 6 – 10 ).

Figure 1 illustrates some of the areas in which UAS photogrammetry can be used. Most relevant to this project is the use of UAS for “construction progress monitoring” that has recently been embraced by the AEC/FM industry. “Automated” progress monitoring research is a young area of scientific interest, although many of the data acquisition and analysis technologies have been studied in a large part of the available literature. It provides a clear view of construction processes to prevent cost deficiencies ( 11 ).

Figure 1.

Unmanned aerial systems (UAS) applications in different areas.

This research effort is the third phase of a project supported by Michigan Department of Transportation (MDOT), U.S., and builds on recently completed research in using UAVs to capture and process visual inspection data. MDOT has successfully shown the ability of UAV-based data collection to be a useful approach to support decision-making for infrastructure condition assessment, asset management, and systems operations ( 12 – 14 ). It was concluded that UAVs are capable of providing timely and cost-efficient data for supporting MDOT decision-making. Extending Phases I and II of MDOT projects, this paper is aligned with the objective of the ongoing phase of the research project “Integrating UAVs into MDOT’s Day-to-Day Usage for Transportation Infrastructure Evaluation and Management Purposes.”

The main motivation of this research is to illustrate a method that uses UAS-based construction inspection to assist a project manager in monitoring project progress during the construction phase. As a secondary outcome, the method also allows for the development of as-built models post-construction.

A critical difference between the existing literature and this paper lies in the underlying effort at integrating and mapping the data collected by UAS BIM for Road Parameters. Data structures in Industry Foundation Class (IFC) “Road” are employed to support this paper’s methodology. IFC is an integrated model schema that describes construction information ( 15 ). IFCs are developed by buildingSMART to solve the data integration problem in the construction sector. Figure 2 is a process diagram mapping UAS data onto BIM parameters and provides the missing link in this process. Within the scope of this research, UAS data are associated with their corresponding parameters in BIM, and geometric parameters of as-built BIM models are automatically updated. Using this updated BIM model, as-planned versus as-built progress of the project are compared to identify progress deviations. This enables the project manager to make decisions (corrective actions, such as expediting the project or adding more resources, in an iterative manner) to set the project back on track and prevent delays or cost overruns.

Figure 2.

Process diagram showing scope of this paper.

Literature Review

In this section, some of the associated studies with applications of UAS in the AEC/FM industry are reviewed. The emphasis of this paper is on approaches used in BIM and automatic as-built modeling and project management and construction progress monitoring using UAS. Table 1 is an annotated list of some of the relevant bodies of literature that this research will build on. It is classified by topic area and the year in which the work was published. These are the main studies being referred to in this document.

Table 1.

Associated Studies with Unmanned Aerial System (UAS) Applications

Application	Author	Takeaways and gaps
Construction progress monitoring	Kluckner et al. ( 16 )	A user study, with expert users from construction companies, can be done to verify the designed progress monitoring procedure.
	Zollmann et al. ( 17 )
	Zollmann et al. ( 18 )	The main focus of visualization has been on planar objects. Other shapes need to be studied as well.
	Han et al. ( 1 )	Change detection data has the potential to support the visualization data.
	Lin et al. ( 19 )
	Omar and Nehdi ( 20 )	3D modeling is being used, while having 4D models makes progress monitoring straightforward.
	Hamledari ( 21 )	High accuracy rates and real-time performance are among the advantages of using UAVs for construction progress monitoring.
	Wang et al. ( 22 )
Project management	Vacanas et al. ( 23 )	UAS has the potential to enhance the current practices of project control and condition assessment in the AEC/FM industry, by using the technical challenges addressed.
	Ham et al. ( 24 )
	Irizarry and Costa ( 25 )	Working with different levels of data quality needs to be studied.
	Hubbard and Hubbard ( 26 )	The UAS-collected data can be required as a deliverable in DOT highway construction contracts, for project management purposes.
Infrastructure management	Bendea et al. ( 27 )	There is a potential for improving processing speed of meta data collected by UAS.
	Tatham ( 28 )
	Gao et al. ( 29 )
	Matsuoka et al. ( 30 )	Geological hazards investigation potential application of UAVs in the complex climatic and geographic conditions need to be studied.
	Dobson et al. ( 2 )
	Dobson et al. ( 31 )	UAV implementation in MDOT workflows is identified beneficial in improving cost-effectiveness, operational management, and timely maintenance of Michigan’s highway infrastructure.
Automated as-built modeling	Golparvar-Fard et al. ( 3 )	Fully automatic object extraction algorithms are missing.
Automated as-built modeling	Pătrăucean et al. ( 32 )	Joint interdisciplinary efforts are needed for developing new object recognition methods.

Note: AEC/FM = architecture, engineering, construction, and facility management; DOT = department of transportation; MDOT = Michigan Department of Transportation; UAS = unmanned aerial system; UAV = unmanned aerial vehicle.

BIM and Automatic As-Built Modeling

In the last few years, BIM has become an official standard in the construction industry as a means of information exchange for construction assets. There are several definitions of BIM. It has been defined as: a process of creating and managing parametric digital models of a building during its lifecycle; digital visual representation of all of the physical characteristics of a building through its lifecycle; a set of interacting policies, processes, and technologies generating a methodology to digitally manage the essential building design and project data; or the parametric 3D computer-aided design (CAD) technologies in the AEC industry ( 33 – 36 ). There has been a growing interest in generating automatic representations for a project lifecycle among different stakeholders, in particular industrial, academic, and governmental parties. Although creating building information models (BIMs) generated in the design stage of a facility—or as-designed BIMs—is becoming increasingly common, generating BIMs that reflect a facility in its as-built conditions is a complicated process. Facilities which are not adequately equipped with as-designed BIMs, or the ones for which the as-built conditions are different from their as-designed BIM, are demanding for as-built BIMs ( 32 ). While as-designed BIMs were first introduced in the 1970s, gaps in the data collection, storage, and modeling (both the technology and knowledge) hindered the development of as-built BIMs and limited the number of existing as-built BIMs to a small number.

One of the seminal studies on automatic as-built modeling was done by Pătrăucean et al. with a focus on geometric modeling ( 32 ). They divided the as-built model creation process into two steps of: (a) data collection, to capture the as-built conditions, and (b) data modeling, to generate rich visualizations understandable by other processes. For those cases where an as-designed BIM is available, the main part of the as-built modeling is to register the point cloud sampled from the as-designed model, with the point cloud completed by 3D reconstruction. In the absence of an as-designed model, different communities of computer vision, geometry processing, and civil engineering address different approaches to detect and recognize the large number of infrastructure elements of the project. Of the future research directions, one would be the consolidation and integration of the existing techniques, in combination with developing new methods that integrate with decision-making workflows.

Golparvar-Fard et al. studied integrated sequential as-built and as-planned models to be used in the decision-making process in the AEC/FM industry ( 3 ). Their study was focused on: (a) automated geo-registration of site images and creating as-built cloud models from images taken daily on the job site, (b) automated 4D as-built point-cloud models’ generation, and (c) semi-automated integrated 4D as-built and as-planned visualization creation adding time to the as-built model, to develop a 4D augmented-reality (D4AR) model. The limitations and benefits of these models, as well as their applications, were studied through seven case studies. An open question is how to fully integrate as-built and as-planned models, including 4D (3D + time) data into decision-making workflows. Automation will result in a less complicated process for studying as-builts of a project and tracking of the project progress.

Construction Management and Progress Monitoring Using UAS

Many stakeholders are involved in a transportation infrastructure project, and the difference between their perceptions of the construction processes means project management of this type of project faces several issues. Contract delays and related disputes are among the fundamental issues in infrastructure projects.

Vacanas et al. studied the use of BIM and UAS technologies for as-built data collection methods and record-keeping tools for construction delay analysis and mitigations ( 23 ). Rapid 3D representation, increased availability of decision-making data, accurate and fast change updates in the different phases of a project, reduction of labor-hours, and improved communication among the project team are among the advantages of using these technologies ( 34 ). This study stated that using UAS for updating the project as-built models is more efficient than the expensive and complicated manual process of work progress monitoring carried out by the engineers on site. Two questions remain unanswered: what is the workflow, and how can data structures be automated to associate UAS data with BIM?

Visual data acquisition and analytics using UAS for automated progress monitoring has also been studied ( 28 ). By addressing the lack of frequent and complete site images, as the main gap for automated progress monitoring purposes, the authors introduced UAS as a popular technology for collecting site images. Two real-world construction project examples are studied in their research to investigate the applications of UAS in enriching 4D BIM models containing spatial and temporal information of the project. There are several challenges that need to be addressed: (a) issues concerning BIM elements detection, (b) restricted visibility of elements in close-range images, (c) training and experience needed for site photography, and (d) high cost of the complete documentation in manual photography.

In other applications of UAS for construction management tasks, four visual database development case studies in the U.S. and Brazil used semi-structured interviews with construction project teams ( 25 ). The research studied the costs related to using UAS: the cost of UAS itself, in addition to certificate of authorization cost, insurance cost, and operation cost, are the main cost items associated with UAS use on construction sites. Additional data was needed to perform detailed comparisons with current imaging methods.

This research departs from the existing research by addressing gaps in data collection and modeling, and their translation into useful digital models that can support decision-making. Creating as-builts from UAS images is also time and effort consuming. In addition, the principal problem lies in automating progress monitoring workflow, by integrating different data structures. There is no research on processing imagery from UAS for creating as-built models in an automated manner. This paper takes advantage of feature extraction technologies and a relatively simple mapping approach to develop the foundations for automating the process of using UAS photogrammetric data for decision-making purposes.

Mapping Methodology

This section presents the method to structure and process the UAS-collected data into meaningful parameters that can be used to support decision-making metrics and support the development of as-built BIM models. UAS is used to collect visual data (images/videos) by flying over the relevant locations of the project site. The imagery is processed through structure from motion (SfM) photogrammetric software packages, such as Agisoft PhotoScan, to create an orthophoto, point cloud, and 3D models. The data items are used as input for feature extraction processes to produce geometry parameters. Thanks to the recent advances in computer vision, there are numerous feature extraction algorithms in the literature. This research effort utilizes feature extraction algorithms, but the focus is on data collection and processing techniques as it applies to construction decision-making. MATLAB’s Image Processing and Image Labeling application/toolbox is used to extract features such as lines and circles that have been identified for extraction. The scope of this paper does not include an analysis and comparison of different feature extraction algorithms.

The driving question in this section is: what are the features that need to be extracted from the data collected by the UAS? As the objective of this paper is to aid with real-time progress monitoring, the need is to extract geometric features, such as lines, shapes and areas, that can be used to estimate the quantities of work completed for each construction activity. At each step during the construction process, the progress can be estimated by comparing the estimated quantity of work completed to the as-planned quantity of work that should have been completed at that point in the schedule. Therefore, another input is the as-planned schedule of work, which is simply the baseline for the activities. In addition, the information on the technical construction specification and pay items associated to each activity is utilized. This as-planned information is linked to pay items and used as a benchmark in the progress comparison process. Earned value management (EVM) metrics can then be used to quantify the project progress using of cost and schedule. This method is explained in detail in the next subsections.

A secondary objective is to use generated 3D as-built models of the constructed assets during the last inspection run on project completion. Therefore, choice of the data extracted should be suitable for association with parameters supporting BIM to represent the geometry and functionality of the constructed assets. IFCs are employed as a data exchange standard to guide the selection of the required BIM parameters and their relationships. In particular, IFC for Road is capable of identifying the spatial and physical components of a highway. Therefore, the data extracted from the UAS data are closely aligned to the parameter definitions in IFC for Roads. This will ensure a standardized approach to structuring the data and allow for seamless integration with BIM for long-term asset management purposes.

Figure 3 shows the proposed plan for data collection, mapping, and management. Step 1 and Step 2 involve on-site data collection using a UAS. The site is selected, and the start of the project time is noted as T₀. The site is surveyed, and for each of the activities the UAS is flown at regular intervals and the data is collected at each successive time point with time stamps of T₁, T₂, T₃ … and so on. The number of UAS passes, that is, the granularity of data collection, will depend on the nature of the project. At the time of writing this paper, the site data collection was delayed because of the pandemic. However, it is expected that the measurements will be made in short intervals of 1 hour between time stamps early in the project to estimate production rates, and then at intervals of 8 hours as the production rate estimates become more stable.

Figure 3.

Proposed data collection, mapping, and management plan.

Photogrammetric data collected by UAS need to be processed and turned into numerical geometry parameters such as lengths and areas. Figure 4 is an example of an image taken from a concrete paving operation. Identifying the length and width (and thickness if needed) of the lane paved makes it possible to calculate the progress on a construction activity. Table 2 illustrates the different geometric parameters that will be extracted automatically from the photogrammetric data for typical highway construction activities. These parameters are aligned with the pay items and inspection requirements as per MDOT Standard Specifications for Construction ( 37 ).

Table 2.

Master List of Parameters

Construction activity			Parameter(s)
Category	Pay item	Unit	Parameter(s)
Earthwork	Earth excavation	Cubic yard	Length embankment	Width embankment	Height embankment
Concrete pavement	Topsoil Stripping	Square yard	Length stripped area	Width stripped area	na
	Pavement breaking and removal	Square yard	Length pavement removed	Width pavement removed	na
	Pavement	Square yard	Length lane paved	Width lane paved	na
	Overlaid lane furnishing and curing	Square yard	Length overlaid lane finished/cured	Width overlaid lane finished/cured	na
Base	Base removal	Square yard	Length base removed	Width base removed	na
	Reinforced base course, inch	Square yard	Length reinforced base course	Width reinforced base course	na
	Nonreinforced base course, inch	Square yard	Length nonreinforced base course	Width nonreinforced base course	na
Subbase	Subbase removal	Square yard	Length subbase removed	Width subbase removed	na
	Reinforced subbase course, inch	Square yard	Length reinforced subbase course	Width reinforced subbase course	na
	Nonreinforced subbase course, inch	Square yard	Length nonreinforced subbase course	Width nonreinforced subbase course	na
Shoulder	Reinforced concrete shoulder	Square yard	Length reinforced shoulder paved	Width reinforced shoulder paved	na
Shoulder	Nonreinforced concrete shoulder	Square yard	Length nonreinforced shoulder paved	Width nonreinforced shoulder paved	na
Sidewalk	Sidewalk removal	Square yard	Length sidewalk removed	Width sidewalk removed	na
Joint	Joint (type)	Foot	Length joint	na	na
Joint	Sawing joints	Foot	Length sawed joint	na	na

Note: na = not applicable

Figure 4.

Unmanned aerial system (UAS) numerical factors for concrete pavement activity.

Next, Figure 5 shows a typical two-lane highway corridor section view. A corridor comprises of an assembly of mainline pavement, median, shoulder, sidewalk, and daylighting. To model these components through BIM, widths and slopes are required as primary geometrical attributes. Figure 6 presents a list of primary parameters required for modeling a road assembly and subassemblies. These parameters are extendable to any other highway component. Based on the road component, and the related construction activity, the IFC for road entities can be employed to develop the BIM parameters. For instance, IfcRoadElement provides a structure for modeling physical elements of a highway, including mainline and shoulders. Main IFC entities used in this paper are summarized in Figure 7.

Figure 5.

Highway corridor section view.

Figure 6.

Highway corridor modeling parameters (east-bound and west-bound, two-lane).

Figure 7.

Main Industry Foundation Class (IFC) structures used.

The geometry parameters to be extracted from the UAS data (as illustrated in Table 2) map on to the BIM parameters as illustrated in Figures 6 and 7. This mapping is illustrated in Figure 8. IFC for Road includes two main groups of physical and spatial elements. Mainline parameters, as well as shoulder, median, and sidewalk, are mapped to their associated IfcRoadElement parameters.

Figure 8.

IfcRoadElements mapped to corridor parameters.

Having extracted geometry parameters from the UAV data and ensuring that they can be mapped to BIM parameters, the next step is to develop functions that calculate progress metrics using EVM, a commonly used technique for progress measurement in the construction industry ( 38 ). If applied properly, this technique provides project managers with early performance deviation warning. Cost variance (CV) and schedule variance (SV) are the two main metrics used in this research. CV is used to calculate how much over or under the budget the project is. SV is an indicator of whether the project is ahead or behind the schedule. Three measures are used to calculate CV and SV as described below:

– Actual cost of work performed (ACWP): measures the performed work in relation to budgeted cost.

– Budgeted cost of work performed (BCWP): measures the performed work in relation to actual cost. It is also called earned value (EV).

– Budgeted cost of work scheduled (BCWS): measures the scheduled work in relation to budgeted cost. Using these three measures, the CV and SV indicators are calculated (Equations 1 and 2). These two indicators are integrated with the proposed methodology and used as progress metrics.

CV = ACWP - BCWP

(1)

SV = BCWP - BCWS

(2)

The progress monitoring is done based on benchmarked as-planned schedule data. This is illustrated with an example in the next section.

Example Project Implementation

This research is currently being implemented for the I-496 concrete pavement reconstruction project in Michigan. A section of this highway (Mainline, Sta. 180+00–335+00) for about 3 mi, is divided into two EB and WB phases, and the second phase of the WB project is currently being monitored. Table 3 presents this phase’s project information, limiting the focus to seven main activities: (1) pavement removal, (2) culvert installation, (3) earthwork, (4) underdrain installation, (5) separator and open-graded drainage course (OGDC) installation, (6) mainline paving, and (7) shoulder paving. Duration of each activity, precedence activities, and spatial limitations between activities are summarized in Table 3. While the primary focus of this paper was on pavement removal and placing activities (activities 1, 6, and 7), a hypothetical dataset is used for the other activities (activities 2, 3, 4, and 5) to illustrate the progress monitoring methodology. It is assumed that project documents provide as-planned progress of these activities. Figure 9 illustrates the percentage completion of the project versus time, and time versus stations for project activities. These data have been reported as per the project documents provided by the contractor.

Table 3.

Activities’ Information and Constraints between Them

No.	Activity description	Duration (days)	Precedence activities	Spatial distance between activities
1	Removing pavement	12		na
2	Installing culverts and sewer	7	1	na
3	Performing earth excavation/placing sand subbase	12	1, 2	na
4	Installing open-graded underdrain	9	3	na
5	Placing separator and OGDC	11	4	1 mi between grade subbase and placing OGDC mainline
6	Paving mainline	10	5	1 mi between placing OGDC mainline and paving east bound mainline
7	Paving shoulders and crests road ramps	17	6	To 3 mi between paving mainline and placing shoulder

Note: na = not applicable OGDC = open-graded drainage course.

Figure 9.

As-planned progress versus time, and time versus stations.

The first step is to identify BIM parameters and map them onto UAS factors extracted from images. For this purpose, a list of parameters is created. For creating this list, IFC Road is used as a support. Referring to MDOT’s Standard Specifications for Construction book, Section 602, progress parameters are developed for each activity (Table 4).

Table 4.

I-496 Project Parameters

Activity	Measurement unit	Parameters
Pavement breaking and removal	Square yard	Length pavement removed
Pavement breaking and removal	Square yard	Width pavement removed
Culvert installation	Foot	Length culvert
Earthwork	Cubic yard	Length ditch
		Width ditch
		Thickness ditch
Open-graded underdrain	Foot	Length underdrain
Placing separator and OGDC	Foot	Length separator and OGDC
Mainline pavement	Square yard	Length lane paved
Mainline pavement	Square yard	Width lane paved
Shoulder pavement	Square yard	Length shoulder paved
Shoulder pavement	Square yard	Width shoulder paved

Note: OGDC = open-graded drainage course.

DJI Mavic 2 Pro and Microdrone md4-1000 multirotor UAS with up to 25 min and 45 min of flight time, respectively, are utilized to collect UAS data for this project. Based on each activity’s length and production rate, and the technical factors of flying UAS, a flight path is developed to collect the planned parameters. In this example, two data collection flights are planned for each activity. At two time points (T1 and T2), each activity is observed for a specific duration, and UAS data is collected. This duration is later used to calculate the actual production rate of each activity for each time point. Actual and planned production rates are defined as (Equations 3 and 4):

\begin{matrix} Actual Production Rate = \\ \frac{Quantity of work observed by UAS}{Duration of observation} \end{matrix}

(3)

\begin{matrix} Planned Production Rate = \\ \frac{Quantity of work per project plans}{Duration of observation} \end{matrix}

(4)

Figure 10 is an example of UAS image data collected for mainline pavement activity. Quantity of work done between T₁ and T₂ is estimated using the Image Labeling tool in MATLAB. Using this tool, the equipment and their location are detected and labeled. By tracking this equipment, it is possible to estimate the distance traveled by them and relate it to the length of pavement being placed. For instance, Figure 11 illustrates the “paver” being labeled and tracked, to estimate the length of pavement, as shown in the picture.

Figure 10.

Mainline pavement activity (equipment is labeled).

Figure 11.

Mainline pavement activity timelapsed.

Using Figure 11 (images at T₁ = 11:13 and T₂ = 14:03) and image labeling:

Length of Lane Paved = 973.52 (ft);

Width of Lane Paved = 26.87 (ft);

Duration = 14 : 03 - 11 : 13 = 2 : 50 = 2.83 hours

therefore:

Q u a n t i t y o f P a v e m e n t A c t i v i t y = 973.52 (f t) \times 26.87 (f t) = 26, 158 s q u a r e f e e t = 2, 906 s q u a r e y a r d

And substituting Equation 3:

\begin{matrix} Actual Production Rate = \\ \frac{2906 (square yard)}{2.83 (hours of observation)} = 1025.82 sq . y / hr \end{matrix}

Planned production rates are extracted from project documents. For instance, for mainline pavement of 57,400 square yard (quantity extracted from plans) in 10 days (duration of activity), the production rate using Equation 4 is:

\begin{matrix} Planned Production Rate = \\ \frac{57, 400 (square yard)}{10 (days) \times 8 (hours per day)} = 712.55 sq . y / hr \end{matrix}

The same procedure is repeated for “pavement breaking and removal” (activity 1) and “shoulder pavement” (activity 7). Actual and planned production rates are estimated from UAS-collected data, by processing the images and using the Image Labeling tool in MATLAB.

Another factor is the unit cost of each activity. The estimated inputs for each activity are summarized in Table 5. Using these inputs, the EVM metrics are calculated for the project (at time T₁ and T₂) and summarized in Table 6. At both time points, cost variances are negative, indicating that project was over the budget (−3.08% and −3.56%). Contrary to cost deviation, schedule variance is positive for both time points (6.31% and 8.85%), indicating that the project is ahead of the schedule (Figure 12).

Table 5.

Results (Readings at T₁ and T₂)

Activity	Variable	Unit	T1	T2
Activity	Variable	Unit	Amount	Amount
Pavement breaking and removal	Quantity	Square yard	1,749.29	1,825.62
	Planned production rate	Square yard per hour	321.85	321.85
	Actual production rate	Square yard per hour	423.22	441.68
	Budgeted unit cost	$	50.00	50.00
	Actual unit cost	$	51.00	50.63
Culvert installation	Quantity	Foot	1,766.67	1,033.33
	Planned production rate	Foot per hour	432.00	432.00
	Actual production rate	Foot per hour	294.44	258.33
	Budgeted unit cost	$	66.67	66.67
	Actual unit cost	$	69.53	70.97
Earthwork	Quantity	Cubic yard	2,204.00	2,157.33
	Planned production rate	Cubic yard per hour	1,200.00	1,200.00
	Actual production rate	Cubic yard per hour	1,102.00	1,078.67
	Budgeted unit cost	$	150.00	150.00
	Actual unit cost	$	151.32	150.00
Open-graded underdrain	Quantity	Foot	2,682.33	2,646.67
	Planned production rate	Foot per hour	2,700.00	2,700.00
	Actual production rate	Foot per hour	2,682.33	2,646.67
	Budgeted unit cost	$	260.00	260.00
	Actual unit cost	$	261.43	262.00
Placing separator and OGDC	Quantity	Foot	3336.00	7001.33
	Planned production rate	Foot per hour	3,600.00	3,600.00
	Actual production rate	Foot per hour	3,336.00	3,500.67
	Budgeted unit cost	$	180.00	180.00
	Actual unit cost	$	184.09	187.50
Mainline pavement	Quantity	Square yard	2,906.50	2,866.79
	Planned production rate	Square yard per hour	712.55	712.55
	Actual production rate	Square yard per hour	1,025.82	1,011.81
	Budgeted unit cost	$	270.00	270.00
	Actual unit cost	$	289.67	289.00
Shoulder pavement	Quantity	Square yard	860.00	867.78
	Planned production rate	Square yard per hour	230.16	230.16
	Actual production rate	Square yard per hour	303.53	306.27
	Budgeted unit cost	$	200.00	200.00
	Actual unit cost	$	201.98	202.68

Note: OGDC = open-graded drainage course.

Table 6.

Earned Value Metrics

Parameter	Description	Time point
Parameter	Description	1	2
BCWP	Budgeted cost of work performed	$2,790,489.41	$3,379,735.63
ACWP	Actual cost of work performed	$2,876,554.24	$3,499,927.10
BCWS	Budgeted cost of work scheduled	$2,624,849.06	$3,215,246.18
CV	Cost variance	$(86,064.83)	$(120,191.47)
% CV	Deviation from budget	−3.08%	−3.56%
SV	Schedule variance	$165,640.35	$284,680.92
%SV	Deviation from schedule	6.31%	8.85%

Figure 12.

BCWP, ACWP, and BCWS.

Conclusions and Future Research

UAS visual data collection and analysis is a fast and cost-effective method for creating construction progress information models. Using image processing and image labeling tools, the photogrammetric data is directly converted to parameters that can support progress monitoring decision-making. This paper presents a mapping methodology for automating progress monitoring by associating photogrammetric data with BIM models. The I-496 MDOT project is used as a case study to illustrate application of the proposed methodology. The primary beneficiaries of this research are the DOT project managers and inspectors. The research can also be used for coordination and communication between owners and contractors in a construction project.

The mapping framework proposed in this paper is a crucial step toward the creation of BIM. The methods discussed in this paper for estimating completion quantity, EV analysis, and project status determination, using UAS data collected and mapped on to BIM, provide a foundation for the eventual development of 3D as-built models and 4D construction progress models. Future work will further examine applications of the proposed methodology in real-world case studies, and address potential challenges of archiving and analyzing construction information. The authors are also working on extending the mapping methodology to other transportation elements, such as highway bridges.

Footnotes

Acknowledgements

All support by MDOT and Michigan Tech Research Institute (MTRI) are gratefully acknowledged, including Jason Early, Steve Cook, and Andre Clover at MDOT for project advice, and Rick Dobson and Chris Cook at MTRI for UAS data collection.

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: R. Samsami, A. Mukherjee, C. Brooks; data collection: R. Samsami, A. Mukherjee, C. Brooks; analysis and interpretation of results: R. Samsami; draft manuscript preparation: R. Samsami, A. Mukherjee. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work presented in this paper is a part of an ongoing project funded by MDOT, research project number OR19-064, contract and authorization numbers 2019-0311/Z1, titled “Integration of Unmanned Aerial Systems Data Collection into Day-to-Day Usage for Transportation Infrastructure.”

ORCID iD

Colin N. Brooks

Any opinions, findings, conclusions, or recommendations presented in this paper are those of authors and do not necessarily reflect the views of the funding agencies.

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