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This research presents a novel microlevel (i.e., project-level) construction productivity framework aimed at measuring and benchmarking productivity consistently within and between projects, work packages, and activities. Using a design science research approach, the framework was rigorously developed with data collected from UK-based construction projects and refined through several cycles of implementation and industry engagement. The framework is useful in providing new insights into project performance, helping understand process variability, setting benchmarks, and conducting data analytics for improved decision making. This framework can be used for industrywide measurement both within and across subsectors of construction in the UK and internationally. [ABSTRACT FROM AUTHOR]

Productivity assessment plays a key role in successful earthwork projects and is primarily achieved by monitoring key construction equipment such as excavators and dump trucks. Vision-based methods are widely adopted to analyze the productivity of construction equipment in earthwork projects. These methods are inexpensive and easy to implement and maintain. However, previous studies on vision-based productivity analysis of earthmoving equipment have predominantly demonstrated its effectiveness under favorable conditions characterized by stable lighting, areas devoid of shadows, and high-visibility environments free from significant occlusion by other objects or terrain. There is a significant illumination difference and limited visibility under harsh low-visibility conditions at earthwork construction sites, which makes it difficult to achieve reliable identification accuracy using these already developed methods. To address this problem, this study proposes an automatic vision-based dump truck productivity measurement method based on a deep learning illumination enhancement algorithm combined with transfer learning for low-visibility, harsh conditions at earthwork construction sites. This method uses an internet of things (IoT) system equipped with a camera to capture the image and a deep learning illumination enhancement algorithm, trainable deep hybrid network (TDHN), to enhance the image quality under low-light conditions at earthwork sites. Then, a deep convolutional neural network image recognition algorithm, ResNet50, was combined with a transfer learning technique to extract information from the image. Through the IoT, this processed information is sent to the earthwork platform to perform productivity analysis and make timely decisions regarding equipment allocation schemes. To validate the effectiveness of the proposed methodology, it was implemented in a real-time earthwork project. This study results show that image recognition accuracy of 99.6%, 95.67%, and 94.77% under normal lighting, low lighting, and extremely low lighting conditions, respectively. The dump truck recognition accuracy increased by 1.10%, 3.62%, and 21.19%, leading to a significant improvement in productivity measurement of 1.08%, 3.54%, and 20.71% for the above-mentioned lighting conditions, respectively. [ABSTRACT FROM AUTHOR]

Activity-level productivity measurement often overlooks losses due to poor construction flows—that is, the flows of workers, materials, and equipment. Using data from 138 locations and 33 crews, correlation and regression analyses were conducted on several flow metrics. The results showed that excess work-in-progress time, representing handoffs among crews, and work discontinuities, representing stoppages between locations, had the most significant association with productivity. These findings led to the development of a novel framework for measuring activity-level construction productivity. In this framework, productivity is decomposed into efficiency (measured through work sampling) and effectiveness (measured as increased output). We applied our framework to a building construction project featuring four structural work crews operating across multiple levels. The results indicate that productivity losses due to crew handoffs and work discontinuities decrease as work becomes more repetitive. Additionally, inefficiencies or idling occur when moving equipment is unavailable, whereas ineffectiveness occurs with high rework. Our framework enables the identification of various sources of productivity loss. It also helps understand that the direct work rate is just one component of overall productivity. [ABSTRACT FROM AUTHOR]

Productivity, defined as output per input, is a key performance indicator. Construction is one of the least productive industries, and in this paper we explore how productivity measurement can be improved. The findings are based on a review of more than 100 studies. First, we summarize the indicators and data sources used to measure productivity at the macro (economic) and micro (more detailed) levels. Then we explore industry-level productivity trends. Contrary to popular belief, US construction productivity has not declined or stagnated over time. Also, the US has consistently outperformed the UK, Germany, and France. Next, we identify the most critical factors affecting construction productivity. These are related to labor, equipment and technology, construction site, schedule, supervisors, and materials. We also discuss new technologies that can help improve productivity: off-site construction, building information modeling, material tracking technologies, and automated productivity monitoring. Finally, we discuss challenges in this area: the unsuitability of labor productivity as an indicator, issues with activity-level productivity analysis, the disconnect between macro and micro levels, and the lack of micro-level productivity databases. [ABSTRACT FROM AUTHOR]

Large-scale public construction projects often become expensive white elephants requiring considerable postconstruction maintenance. Thus there is a pressing need to integrate construction and postconstruction stages into productivity measurement and analysis. However, previous studies of productivity measurement focused on the efficiency of the project delivery phase, but overlooked the utilization of the constructed facility. This paper developed a two-stage construction project productivity (CPP) measurement model by integrating site efficiency (SE) and utilization effectiveness to investigate strategies for improving the overall CPP. An as-usual setting, which delineates the measurement of CPP, was developed as the benchmark model. Given the relative nature of the productivity concept, two scenarios were considered for CPP improvement. The enhancement scenario was introduced by adding a requirement that CPP is enhanced compared with that in the benchmark model. Then a transcendence scenario was developed to ensure that CPP is better than SE. Strategies for CPP improvement were formulated and were analyzed using the analytical models developed. This study for the first time distinguished the substitutionarity and complementarity between facility occupation and facility management in the postconstruction stage, and claims that the opposite natures offer opposite strategies for CPP improvement. The empirical analysis revealed the law of diminishing marginal utility of facility management. This paper contributes to the body of knowledge by extending the productivity measurement model from the construction stage only to the integration of the construction and postconstruction stages, and by offering two productivity improvement scenarios for strategy formulation. Methodologically, this paper offers a two-stage productivity measurement model and its improvement models in a mathematical way. Managerially, based on the developed models, this work offers the evidence and quantitative guidance for the policy makers and public clients to strategically improve CPP of public projects. [ABSTRACT FROM AUTHOR]

Changes in projects often affect planned performance. Earned value management (EVM) is a performance measurement methodology to control costs and scheduling by evaluating project progress using monetary values. Complementary methods to control project duration in time units were developed. This study contributes to project management by showing how to control costs, schedules, and time variances, given changes in quantity, resource costs, and productivity to identify the responsibilities of a contractor and owner for variations in time and cost. This study addresses the main methods for controlling project time variance in time units, as obtained through an extended literature review, and utilizes these methods by applying data from a real-life project. The findings indicated that in the case of a linear distribution of planned values, four of the five methods yielded identical values for the time variance to date and one of the methods depends more on the number of parallel activities in progress or completed. [ABSTRACT FROM AUTHOR]

Concrete precast plants require strict control over and adherence to the timing and sequence of operations. Variation for this research is divided into the variation in task starting time (the difference between the planned and the actual starting time) and the variation in task duration (the difference between the planned and the actual task duration). This study determined causes of variation in task starting time and duration of precast concrete slab production tasks. It also identified penalties associated with not reducing variation, which include an increase in project duration, Work in-Progress (WIP) and cost and a decrease in labor productivity. Additionally, two execution policies (keeping laborers waiting before preconditions are ready and keeping laborers busy) in the face of variation were compared by using STROBOSCOPE simulation techniques. It was found that simply keeping workers busy is insufficient for managing variation. Management effort should be devoted to eliminate causes of variation in the planning stage to make a reliable work plan. The results will help prefabricators to understand causes and penalties of variation, which is the starting point of attacking and reducing it. Although this study is based on a concrete slab production facility, this research can have a broader effect on the construction industry because the research method and simulation models developed in this study are applicable to other fabrication processes as well. [ABSTRACT FROM AUTHOR]

Frequent and regular prediction of productivity is needed to effectively manage construction operations in progress. This need is proven by significant deviations in productivity between estimated values and actual values, and the dynamic and stochastic changes in productivity over time. Five statistical methodologies that are appropriate for contemporaneous time series were cross validated in the present study. Validation was conducted by comparing the performance of forecasting models constructed using the methodologies. Performance was measured by evaluating the residual sum of squares and the correlation coefficients. As a result, univariate time series analysis was found to be the best-performing methodology. The univariate time series model is particularly beneficial in two respects. Using a single series of contemporaneous productivity data in numeric form, it reduces the effort expended in collecting and analyzing data, and improves the objectivity of analysis. [ABSTRACT FROM AUTHOR]

Productivity has a profound impact on projects that depend on time and cost of construction operations. In addition, time and cost estimates are derived from productivity. Thus, accurate prediction of productivity is essential to effectively plan and control construction operations. Predicting productivity of ongoing operations, however, is challenging. Due to dynamic and stochastic changes in productivity over time during construction, frequent and regular forecasting of short-term productivity is critical in managing ongoing operations. The present research investigated the characteristics of series of periodic productivity that should be taken into consideration to effectively predict short-term productivity continually and proactively. Given the identified characteristics, this study reviewed a few potential statistical methodologies that can make full use of contemporaneous time series data related to production for the purpose of predicting short-term productivity by using trend analysis. The methodologies were demonstrated in this paper using an example case, through which data processing and modeling procedure for modeling contemporaneous series data were explained. [ABSTRACT FROM AUTHOR]

This research study aims to identify strategic assets which currently drive and enhance the organizational capabilities of construction firms. There were 258 sets of questionnaires assessing the level of importance given to 106 substantial resources underlying six organizational capabilities of Thai construction firms that were analyzed. Using factor analysis, these 106 items were reduced to 14, which were termed strategic assets. These 14 strategic assets were then classified based on their influence on the six organizational capabilities. The results indicate that Thai construction firms concentrate mostly on developing excellent reputation, creating strong bargaining power with suppliers and subcontractors, and strengthening the firm’s financial stability. However, they do not give much importance to effective risk and investment management, continuous development and innovation, and explicit strategic management. These findings provide in-depth insight to comprehensively understanding a Thai construction firm’s capabilities. These 14 strategic assets should thereafter be used to develop a practical tool for managers of construction firms to evaluate their firm’s strengths and weaknesses as well as to identify strategic assets required to enhance competitiveness in the market. [ABSTRACT FROM AUTHOR]

Labor productivity is a fundamental piece of information for estimating and scheduling a construction project. The current practice of labor productivity estimation relies primarily on either published productivity data or an individual’s experience. There is a lack of a systematic approach to measuring and estimating labor productivity. Although historical project data hold important predictive productivity information, the lack of a consistent productivity measurement system and the low quality of historical data may prevent a meaningful analysis of labor productivity. In response to these problems, this paper presents an approach to measuring productivity, collecting historical data, and developing productivity models using historical data. This methodology is applied to model steel drafting and fabrication productivities. First, a consistent labor productivity measurement system was defined for steel drafting and shop fabrication activities. Second, a data acquisition system was developed to collect labor productivity data from past and current projects. Finally, the collected productivity data were used to develop labor productivity models using such techniques as artificial neural network and discrete-event simulation. These productivity models were developed and validated using actual data collected from a steel fabrication company. [ABSTRACT FROM AUTHOR]

Construction productivity has been a cause of great concern in both the construction industry and academia. Even though many companies have developed their own productivity tracking systems based on their experiences and accounting systems, none have been successful in establishing common definitions and developing a survey tool that collects standard productivity data at the appropriate levels. This research was initiated to establish a common set of construction productivity metrics and their corresponding definitions. As a result of this research effort, the Construction Productivity Metrics System (CPMS), which contain a list of direct and indirect accounts and 56 data elements grouped into seven major categories, was developed. The Construction Productivity Metrics System is a standard construction productivity data collection tool and provides a framework to report industry norms to benchmark construction productivity. Input from 73 industry experts was used in determining the 56 measuring elements and their corresponding definitions. Preliminary findings from initial sample of 16 industrial projects indicate that the productivity metrics can be produced and should be meaningful for construction productivity benchmarking. Because of the small sample size, more than general preliminary conclusion would be inappropriate. Based on the analyses, the developed CPMS is believed to be a reasonable productivity data collection tool and when sufficient data are available should be capable of producing reasonable industry benchmarks. [ABSTRACT FROM AUTHOR]

Bored piles are vital elements for highway bridge foundation. A large number of factors oversees productivity and cost estimation processes for piles, which creates many problems for the time and cost estimators of such process. Therefore, current study is designed to diagnose these problems and assess productivity, cycle time, and cost for pile construction using the artificial neural network (ANN). Data were collected for this study through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered to manage the piling construction process. Three-layer, feed forward, and fully connected ANNs were trained with an architecture of seven input neurons, five output neurons, and different hidden layer neurons. The ANN models were validated and proved their robustness in output assessments. Three sets of charts have been developed to assess productivity, cycle time, and cost. This research is relevant to both industry practitioners and researchers. It provides sets of charts for practitioners’ usage to schedule and price out pile construction projects. In addition, it provides researchers with a methodology of applying ANN to pile construction process, its limitation, and future suggestions. [ABSTRACT FROM AUTHOR]

Continuous flight auger (CFA) piles do not receive sufficient research attention although they are widely used in the United States, United Kingdom, and other countries in the world. Therefore, this paper focuses on describing the CFA pile installation features, exploring its construction methods, and determining the factors that affect the CFA pile productivity and cost. A productivity index (PI) is developed to quantify the effect of subjective factors on the CFA process productivity. Current research concludes a PI value of 0.78 out of 1.0 (efficiency). Several models are designated to assess the CFA pile’s cycle time, productivity, and cost using deterministic approach. Various charts are developed to show this assessment based upon these models. Results show that the cycle time for 12.19 m depth and (0.36 m)(14 in) diameter piles is 16 min; however, its productivity is 24 piles (holes)/day. The developed tools play an essential role in the CFA decision making process. This paper is relevant to both researchers and practitioners. It provides charts for practitioners’ usage to schedule and price out the CFA pile construction projects. In addition, it provides the researchers with a methodology of applying the deterministic approach to the CFA pile construction process and its limitations. [ABSTRACT FROM AUTHOR]

Although Six Sigma has been implemented in the manufacturing and other services industries, it is still a relatively new concept in the building industry. This paper described the Six Sigma concept as a quality initiative that may be applied in the building industry. The principles, methodology, and metrics of Six Sigma are first discussed. The implementation phases as well as the training programs required are explained. A case study of how Six Sigma was pioneered in an organization in the building industry is presented. The findings suggest that management initiative and support, relevant training, appropriate selection of pilot projects, and commitment by team members are crucial for the successful implementation of Six Sigma in the organization. The application of Six Sigma for improving the quality of internal finishes during construction is also explained. Improvement measures taken by a contractor helped to raise the sigma from 2.66σ to 3.95σ for quality of internal finishes. [ABSTRACT FROM AUTHOR]

A large number of problems faces the installation of pile (drilled shaft) foundations: unseen subsurface obstacles, lack of contractor experience, site planning, etc. These problems make it difficult for the estimator to assess the pile construction productivity and cost. Several techniques might be good candidates for this assessment problem. A fundamental question arises: which technique is the most appropriate to solve this assessment problem? This study focuses on answering this fundamental research question. Data were collected through designed questionnaires, site interviews, and telephone calls to experts in different construction companies. Four different techniques were listed as candidates to solve this problem: deterministic, simulation, multiple regression, and artificial neural network (ANN). They were categorized into two groups: process oriented techniques, deterministic and simulation; and data oriented techniques (DOT), regression and ANN. All techniques were used to assess productivity and cost of pile construction. Their results were compared to determine the closest assessment to real world practice. Research results show that the DOT techniques were the most appropriate whereas they had the lowest deviation from real world practice. [ABSTRACT FROM AUTHOR]

This paper details a mathematical algorithm to determine the required number of rollers for a chip seal project’s equipment spread based on maximizing the asphalt distributor’s production and adhering to specified roller time requirements. The paper reports one of the findings of a three-year constructability review of the Texas Department of Transportation’s statewide seal coat program. One of the findings of that study was that there was a correlation between the number of rollers used on a job and the loss of aggregate after construction was completed. The roller is the tool used to seat the aggregate and create embedment in the asphaltic binder. Thus, the failure to achieve design embedment depth is primarily a function of achieving quality control requirements for roller linger time. The paper illustrates a straightforward method to calculate the required number rollers to achieve both maximum productivity for the chip seal paving train and to ensure a uniform distribution of roller coverage across the width of the shot. The paper concludes that designing the roller spread is in the best interests of both the chip seal contractor and the owner. [ABSTRACT FROM AUTHOR]

Risk analysis is crucial in enabling management to make informed decisions regarding the economic viability of engineering projects. In most building construction projects, sophisticated risk assessment methods are not used because the detailed input information they require is unavailable to the average project owner or manager. As a result, risk assessment is often limited to simple sensitivity analyses. This paper presents a practical yet thorough method in which the economic risk inherent in a construction project can be calculated with input information of varying levels of detail. The proposed “multifactor” method includes consideration of interdependence between a project’s risk factors. The principles of the method are explained, its application to a large construction project is illustrated, and the findings are discussed. [ABSTRACT FROM AUTHOR]

Subsurface obstacles, lack of contractor experience, site planning in addition to other factors complicated the installation process of pile foundation. Productivity and cost estimates for such a process are affected by these difficulties. Therefore this study focuses on the piling process productivity and cost assessment using simulation. Data were collected for this study through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered in the piling construction process. Two simulation models have been designed and validated to assess piling process productivity and cost. Consequently, two sets of charts have been developed based upon the validated models to provide the decision-maker with a solid planning, scheduling, and control tool for piling process projects. [ABSTRACT FROM AUTHOR]

The management process of any construction operation usually can be defined as a chain of decisions. A decision-maker can bid, plan, and organize the bored pile projects based on an accurate estimate of productivity. To estimate productivity efficiently, piling process qualitative and quantitative factors have to be considered. This paper focuses on assessing the effect of subjective factors on bored pile construction productivity. A productivity index (PI) model is developed to represent this subjective effect in refining productivity assessment using simulation and deterministic techniques. The analytic hierarchy process and fuzzy logic are used to develop the proposed PI model that relies on the actual performance of 10 main piling process subjective factors. Subjective data were collected from bored pile (drilled shaft) contractors considering these subjective factors. The developed PI model implementation to piling process resulted in PI=0.7. This value has been validated using simulation model outputs. [ABSTRACT FROM AUTHOR]

This paper provides a historical perspective on construction research trends as reflected in the American Society of Civil Engineer’s Journal of Construction Engineering and Management (JCEM). The paper reports the results of an 18-year analysis of JCEM between 1985 and 2002. The analysis focused on such data as author information (affiliations and home countries), as well as the research topics of the technical papers. The paper summarizes the data extracted from the journal and use it to analyze construction research trends. Construction trends reveal that U.S. writers contributed the most papers by that there has been an increasing number of international submissions to the journal. The top research topical areas relate to scheduling, productivity, constructability, simulation, cost control, planning, safety, and computer systems as described in this paper show that research is increasing throughout the world and topics are changing as current needs change. However, there is a need to increase research collaboration between industry and academia, government and academia, and industry and government to advance the construction industry. [ABSTRACT FROM AUTHOR]

The article discusses various topics published within the issue including one on short-term productivity prediction and its forecasting methodologies.