An integrated ergonomics framework for evaluation and design of construction operations

Labor is one of the most critical resources in the construction industry due to its impact on the productivity, safety, quality, and cost of a construction project. Ergonomic assessment, as a tool and method for analyzing human activities and their interactions with the surrounding environment, is thus crucial for designing operations and workplaces that achieve both high productivity and safety. In construction, however, the constantly changing work environments and laborious tasks cause traditional approaches to ergonomic analysis, such as manual observations and measurements, to require substantial time and effort to yield reliable results. Therefore, to simplify and automate the assessment processes, this study explores the adaptation and integration of various existing methods for data collection, analysis, and output representation potentially available for comprehensive ergonomic analysis. The proposed framework integrates sensing for data collection, action recognition and simulation modeling for productivity and ergonomic analysis, and point cloud model generation and human motion animation for output visualization. The proposed framework is demonstrated through a case study using data from an off-site construction job site. The results indicate that integrating the various techniques can facilitate the assessment of manual operations and thereby enhance the implementation of ergonomic practices during a construction project by reducing the time, effort, and complexity required to apply the techniques.     

Fusion of Photogrammetry and Video Analysis for Productivity Assessment of Earthwork Processes

The high complexity of modern large‐scale construction projects leads their schedules to be sensitive to delays. At underground construction sites, the earthwork processes are vital, as most of the following tasks depend on them. This article presents a method for estimating the productivity of soil removal by combining two technologies based on computer vision: photogrammetry and video analysis. Photogrammetry is applied to create a time series of point clouds throughout excavation, which are used to measure the volume of the excavated soil for daily estimates of productivity. Video analysis is used to generate statistics regarding the construction activities for estimating productivity at finer time scales, when combined with the output from the photogrammetry pipeline. As there may be multiple causes for specific productivity levels, the automated generation of progress and activity statistics from both measurement methods supports interpretation of the productivity estimates. Comparison to annotated ground truth for the tracking and activity monitoring method highlights the reliability of the extracted information. The suitability of the approach is demonstrated by two case studies of real‐world urban excavation projects.     

Automated Object Identification Using Optical Video Cameras on Construction Sites

Abstract: Visual recording devices such as video cameras, CCTVs, or webcams have been broadly used to facilitate work progress or safety monitoring on construction sites. Without human intervention, however, both real‐time reasoning about captured scenes and interpretation of recorded images are challenging tasks. This article presents an exploratory method for automated object identification using standard video cameras on construction sites. The proposed method supports real‐time detection and classification of mobile heavy equipment and workers. The background subtraction algorithm extracts motion pixels from an image sequence, the pixels are then grouped into regions to represent moving objects, and finally the regions are identified as a certain object using classifiers. For evaluating the method, the formulated computer‐aided process was implemented on actual construction sites, and promising results were obtained. This article is expected to contribute to future applications of automated monitoring systems of work zone safety or productivity.     

Construction equipment productivity estimation using artificial neural network model

Estimating equipment production rates is both an art and a science. An accurate prediction of the productivity of earthmoving equipment is critical for accurate construction planning and project control. Owing to the unique work requirements and changeable environment of each construction project, the influences of job and management factors on operation productivity are often very complex. Hence, construction productivity estimation, even for an operation with well‐known equipment and work methods, can be challenging. This study develops and compares two methods for estimating construction productivity of dozer operations (the transformed regression analysis, and a non‐linear analysis using neural network model). It is the hypothesis of this study that the proposed neural networks model may improve productivity estimation models because of the neural network's inherent ability to capture non‐linearity and the complexity of the changeable environment of each construction project. The comparison of results suggests that the non‐linear artificial neural network (ANN) has the potential to improve the equipment productivity estimation model.     

A model for automated monitoring of road construction

Monitoring and control of earthmoving operations is gaining an increasing interest. Manual monitoring and control of earthmoving operations have not yielded the expected results. Additionally, because manual monitoring is labor‐intensive, construction managers have to choose between monitoring based on rough estimates, or spending a lot of time collecting and processing data. The latter choice distracts them from many other important duties. The purpose of the present model is to automatically collect and process monitoring data providing the construction manager with real‐time control information. The model was developed for road construction. It uses GPS technology for automated data collection, logging the locations of all the earthmoving equipment while working on the project. Specially developed algorithms convert these locations to control information regarding productivity, duration (or progress) and actual consumption of materials. The model was implemented and tested for 3 weeks in a road construction project. The performance of four activities was measured by the model and compared to manual measurement of the same parameters. This comparison indicated that the model could reach a deviation of ±5%.     

Vision-based workface assessment using depth images for activity analysis of interior construction operations

Workface assessment – the process of determining the overall activity rates of onsite construction workers throughout a day – typically involves manual visual observations which are time-consuming and labor-intensive. To minimize subjectivity and the time required for conducting detailed assessments, and allowing managers to spend their time on the more important task of assessing and implementing improvements, we propose a new inexpensive vision-based method using RGB-D sensors that is applicable to interior construction operations. This is a particularly challenging task as construction activities have a large range of intra-class variability including varying sequences of body posture and time-spent on each individual activity. The skeleton extraction algorithms from RGB-D sequences produce noisy outputs when workers interact with tools or when there is a significant body occlusion within the camera's field-of-view. Existing vision-based methods are also limited as they can primarily classify “atomic” activities from RGB-D sequences involving one worker conducting a single activity. To address these limitations, our method includes three components: 1) an algorithm for detecting, tracking, and extracting body skeleton features from depth images; 2) a discriminative bag-of-poses activity classifier for classifying single visual activities from a given body skeleton sequence; and 3) a Hidden Markov Model to represent emission probabilities in the form of a statistical distribution of single activity classifiers. For training and testing purposes, we introduce a new dataset of eleven RGB-D sequences for interior drywall construction operations involving three actual construction workers conducting eight different activities in various interior locations. Our results with an average accuracy of 76% on the testing dataset show the promise of vision-based methods using RGB-D sequences for facilitating the activity analysis workface assessment.     

A sematic and prior‐knowledge‐aided monocular localization method for construction‐related entities

The real‐time location of construction‐related entities is some of the most useful basic information for automated construction management. However, the implementation of most existing localization methods is limited due to the weak adaptability to construction sites. In this paper, we enhance the monocular vision technique for the localization of construction‐related entities by a sematic and prior knowledge‐based method. A deep learning algorithm is employed to segment the sematic instance in the images, and then the prior knowledge model specifies projection strategies for entities corresponding to various scenarios. Results show that the proposed method achieves satisfying positioning accuracy, is robust in low‐ratio occlusions, and can help facilitate safety early warning, activity recognition, and productivity analysis.     

The measurement of productivity in the construction industry

Productivity in the use of resource inputs is of critical importance to the construction industry. This paper is intended to discuss the relative merits of the most commonly used measures of productivity for the purpose of assessing the productive and allocative efficiency of construction in the 1980s.

The paper concludes that the total factor productivity method is the ideal against which the other approaches should be judged. Both average labour productivity and average capital productivity suffer from serious problems in assessing the efficiency of contracting operations. However, under certain circumstances, either can provide an adequate alternative measure.

Of the two main single-factor measures of productivity, capital productivity appears to be superior in most aspects to average labour productivity as a means of assessing the overall financial management of a construction firm. Notwithstanding the problems associated in contracting activities and also the difficulties inherent in obtaining suitable data, capital productivity is recommended for most circumstances when total-factor productivity measures cannot be applied.     

Interaction analysis for vision-based activity identification of earthmoving excavators and dump trucks

Activity identification is an essential step to measure and monitor the performance of earthmoving operations. Many vision-based methods that automatically capture and explain activity information from image data have been developed with economic advantages and analysis efficiency. However, the previous methods failed to consider the interactive operations among equipment, and thus limited the applicability to the operation time estimation for productivity analysis. To address the drawback, this research developed a vision-based activity identification framework that incorporates interactive aspects of earthmoving equipment's operation. This framework included four main processes: equipment tracking, action recognition of individual equipment, interaction analysis, and post-processing. The interactions between excavators and dump trucks were examined due to its significant impacts on earthmoving operations. TLD (Tracking-Learning-Detection) was adapted to track the heavy equipment. Spatio-temporal reasoning and image differencing techniques were then implemented to categorize individual actions. Third, interactions were interpreted based on a knowledge-based system that evaluates equipment actions and proximity between operating equipment. Lastly, outliers or noisy results were filtered out considering work continuity. To validate the proposed framework, two experiments were performed: one with the interaction analysis and the other without the analysis. 11,513 image frames from actual earthmoving sites in total were tested. The consequent average precision of activity analysis was enhanced from 75.68% to 91.27% after the interaction analysis was applied. In conclusion, this research contributes to identifying critical elements that explain interactive operations, characterize the vision-based activity identification framework, and improve the applicability of the vision-based method for the automated equipment operations analysis.     

Automatic spatio-temporal analysis of construction site equipment operations using GPS data

A literature review revealed several major shortcomings in the analysis of construction equipment operations data, for example, the lack of using realistic or real-time positioning data that can feed into an equipment operations analysis or simulation model. This paper presents technology and algorithms that have the potential in aiding the automated assessment of construction site equipment operations. Utilizing commercially available low-cost global positioning system (GPS) devices enables the continuous data logging of equipment location in addition to simultaneously recording timestamps. However, before any such spatio-temporal equipment data can be reliably collected on construction sites, the error rate of the GPS devices had to be evaluated. Data analysis methods and rules for monitoring construction site equipment operations and activity were then defined. A detailed software interface was finally created that allows a user to set, analyze, and visualize several important equipment parameters towards achieving the goal of creating more realistic equipment operation analysis and potential for inclusion in simulation models. Results from field experiments show that the developed technology is able to identify and track equipment activity- and safety-related information automatically for job site performance and layout decision making, respectively. The presented work will aid construction project managers in making better decisions to plan, manage, and control equipment-related work tasks on construction sites.     

Can project monitoring and control be fully automated?

An extended research programme has examined over the past two decades how productivity in construction projects can be improved through the development of models for project monitoring and control, which process automatically collected data on the actual project performance. Tests that were conducted with these models demonstrate that this approach can help overcome some of the limitations of existing manual methods. However, they also indicate that certain manually obtained data are still required in addition to the automatically collected data. A framework for semi-automated project monitoring and control is proposed, in which both manually and automatically collected data can be incorporated. This framework integrates the monitoring of projects with their control by taking into account the impact on productivity of existing deviations from the planned performance, and of the controlling actions that are proposed to deal with these deviations.     

An integrated system for earthmoving planning

In earthmoving operations, there are a number of activities that need to be considered; for example, path identification, plant selection, assessing the compatibility of the paths and the plants so selected, cost and productivity comparison of alternatives, safety and environmental considerations, etc. The traditional approach to this problem by heuristic rules generated from the experience of planners cannot guarantee an optimal solution. Hence, a scientific method is proposed to automate the earthmoving planning by integrating: (1) a path‐finding algorithm; (2) a plant selection system; (3) application of extenics theory to address the compatibility; and (4) the use of genetic algorithms to optimize the alternatives in terms of costs, productivity, safety and environmental considerations. The integrated system is illustrated step by step using a genuine construction project as an example. The result demonstrates the effectiveness of the system in automating the earthmoving planning exercise.     

Measuring Productivity and Validating Microcomputer Simulation

Abstract: Computer simulation can provide an eficient and cost-effective method of analyzing project alternatives, resource constraints and potential progress. It is frequently performed to study the behavior of a system in an attempt to improve performance. There are many different analytical methods that can be used to plan or analyze a construction operation. However, in most cases, analytical techniques require abstractions which tend to reduce confidence in model predictions. These methods do, however, offer a basis for validating simulation results. This paper illustrates how Micro CYCLONE, a microcomputer-based simulation language, can be used to measure job productivity in construction. It also illustrates how BetaFit, a microcomputer-based program, can be used to model accurately and eficiently random processes for use in the simulation experiments. The paper also presents a framework for validating construction simulation by reviewing the application of two analytical methods. A roof truss process is modeled using the CYCLONE methodology and then validated using queueing theory and method productivity delay modeling to illustrate the various procedures.     

Evolution of the crane selection and on-site utilization process for modular construction multilifts

Modular construction technology has been used in building construction for decades, having been widely utilized on sites with high traffic volume, restricted accessibility/storage, or business operations which cannot be interrupted by long construction time. A key challenge in this method of construction lies in planning and executing lifts within a short timeframe. In this regard, proper crane selection and site layout optimization can significantly increase productivity and shorten the lifting schedule. This paper thus proposes a methodology for the crane selection process and introduces mathematical algorithms to assess the construction of multi-lift operations. The modular lift process is divided into three stages: crane load and capacity check, crane location, and boom and superlift clearance. Each stage's parameters are introduced, analyzed, and graphically explained. The methodology logic is supported by a generalized mathematical algorithm and is applied and tested on a case study involving the construction of five three-storey dormitories in 10 working days for Muhlenberg College in Allentown, Pennsylvania.     

Construction resource/method factors influencing productivity for high rise concrete construction

Research among European countries had confirmed variance in productivity rates ascribed by construction planning engineers for identical operations. Similar differences in planned construction resource/method factors also had been identified. It is hypothesized that such variance may be due to differences in contractor preference, resulting from socio-economic and corporate objective impacts. Analysis of variance and correlation tests are used to examine this hypothesis on data obtained from French, German and UK contractors. Numerous construction resource/method factors are tested for their impact on mean productivity rates for principal high rise in situ concrete construction operations. Significant productivity rate variations are identified for reinforcement fixing and formwork erection, while variance in concrete placing productivity rates are not found to be dependent upon construction resource/method factors. Contractors seeking to improve productivity might wish to consider solutions for construction resource/method decisions that have been found herein to be related to higher productivity rates and (in some cases) lower costs.     

Planning and visualization for automated robotic crane erection processes in construction

This paper summarizes ongoing research aimed at developing knowledge, methods and tools required to implement automated robotic crane erection processes for the construction industry. In the proposed approach, construction cranes are treated as multi-degree-of-freedom robots and modeled in a virtual environment. Virtual cranes are provided with motion-planning algorithms that enable them to find collision-free and time-efficient paths for each piece that needs to be erected. Inverse kinematics are then used to determine the crane motions required to move elements in previously computed paths. By using an effective method to coordinate the tasks and motions of multiple cranes, the system is also extended to construction projects that require simultaneous use of closely-spaced cranes. The virtual crane model provides realistic visualizations of erection processes and detailed erection schedules.     

Simulation-based construction productivity forecast using Neural-Network-Driven Fuzzy Reasoning

Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computing that have significantly helped researchers with decision-making under uncertainties. Uncertainty, an ever-present factor in construction projects, has made such intelligent systems very attractive to the construction industry. Estimating the productivity of construction operations, as a basic element of project planning and control, has become a remarkable target for forecasting models. A glimpse into this interdisciplinary field of research exposes the need for a system, that (1) models the effect of qualitative and quantitative variables on construction productivity with an improved accuracy of estimation and (2) has the ability to deal with both crisp and fuzzy input variables in one single framework. Neural-Network-Driven Fuzzy Reasoning (NNDFR), as one of the hybrid intelligent structures, displays a great potential for modeling datasets among which clear clusters are recognizable. The weakness of NNDFR in auto-tuning the design of fuzzy membership functions along with this model's insufficient attention to the optimization of number of clusters has created an area for further research. In this paper, the parameters (fuzzifier and number of clusters) of the proposed system are optimized by using Genetic Algorithm (GA) to fine-tune the system for the highest possible level of accuracy that can be exploited for productivity estimation. The proposed model is also capable of dealing with a combination of crisp and fuzzy input variables by using a hybrid modeling approach based on the application of the alpha-cut technique. The developed model helps researchers and practitioners use historical data to forecast the productivity of construction operations with a level of accuracy greater than what could be offered by traditional techniques.     

A hybrid optimization mechanism for constructing a dynamic simulation system—An operational behavior analysis of a moving scaffolding system

Standardized production processes in the construction industry can reduce uncertainty, cost and time. The Movable Scaffolding System (MSS), a new bridge construction technique, facilitates automated operations at a high rate. In an MSS, highly repetitive mechanical operations make issues of supply and allocation of machinery and raw materials extremely important. We use Simprocess, a computer simulation software package, to construct hierarchically standardized models of MSS bridge engineering operations (MSSBEO), which encompass bridge superstructure and substructure production, procurement, storage and transmission of raw materials, and utilization of nonconsumable resources such as machines, tools and human resources. We also present a novel dynamic simulation model with a novel fuzzy inference-based optimization mechanism for multiobjective optimization that can identify the optimal resource-allocation strategy under dual goals of minimized time and cost in the MSSBEO model. We analyze the MSSBEO model using the proposed optimization system and use a case study to verify the accuracy and applicability of the proposed system. Through the human decision-making module, optimized resources allocation strategies can be quickly searched to meet actual conditions and enhance the academic value and application of the optimized resource combinations. The most suitable resource combination for an actual situation is generated by applying the proposed optimization module with the fuzzy inference mechanism. After model testing, the optimized resource combinations obtained using the proposed optimization module enhance the utilizations of each resource and reduce the overstocking of materials in the simulation to achieve the goals of reduced cost, efficient work schedules and enhanced productivity.