Journal of Construction Engineering and Management

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.

Site stacking of the prefabricated building components (PBC) should be planned in coordination with installation schedule and other dynamic constraints to improve the overall construction efficiency. This study explores the collaborative planning for stacking and installation of the PBCs (CPSIP) by avoiding collision of the tower cranes that are in the overlapping area and operated simultaneously with the objective of minimizing the prefabricated construction duration. The CPSIP problem, crane-collision cases, collision-avoiding strategy, and multiphase dynamic optimization strategy are analyzed. Then the optimization model for the CPSIP problem subject to the crane-collision avoiding and other dynamic constraints is proposed, and the binary particle swarm optimization (PSO) algorithm is utilized to solve the optimization model for the CPSIP problem. A case study is presented to demonstrate and justify the developed method for obtaining the CPSIP. This study contributes to the knowledge and methodology for achieving the CPSIP by avoiding crane-collision, thus improving efficiency of the prefabricated construction while promoting safety.

Over the past decade, collusive bidding has become more complicated with the development of economies and computer technologies. Understanding the emerging characteristics of collusive bidding helps antitrust authorities advance countermeasures to mitigate such illegal business competition. This study extracted data from 2,007 court judgment documents in China through text mining techniques. Using two-dimensional factors (i.e., project- and colluder-related factors), we performed an effect size-based significance test to demonstrate the new features of collusive practices. The study found that the larger the project size, the more complex the collusive bidding process. In addition, frequent communication between those collusive bidders stemming from adjacent provinces boosts the similarity of collusive biddings between regions. Furthermore, the study disclosed that senior colluders are more intended for those challenging collusive practices. The findings shed some light on feature determination when developing intelligent collusion detectors and offer implications for preventing collusive bidding.

Abstract

Earned value management systems (EVMS) are used by industry practitioners to successfully manage projects and programs; however, there has been no past study to characterize and evaluate the impact of EVMS maturity on project performance. This paper introduces a novel framework, developed to measure the EVMS maturity, composed of 56 attributes arrayed across ten subprocesses, which together make up an integrated project/program management system. This paper describes how the framework was conceived and developed, based on literature review and data collected from industry workshops. Then, it examines the relationship between EVMS maturity and ten project performance metrics through statistical analyses. The study analyzed project data from 35 projects and programs worth over $21.8 billion in total cost. For this sample, results showed statistically significant differences between projects with high and low EVMS maturity; on average, projects exhibiting high EVMS maturity have 54% less cost overruns and 19% less change orders versus their performance measurement baseline. Higher maturity projects and programs also achieve better customer satisfaction, meet business objectives more often, and are more often in compliance with EVMS standards and guidelines. This paper contributes to the body of knowledge by providing a practical and novel framework for assessing the maturity of earned value management systems. Practitioners can use the framework to improve their integrated project and program management.

Practical Applications

A novel framework was developed using inputs from literature sources, a broad survey, and industry practitioners that facilitates the measurement of maturity of earned value management systems (EVMS), and tested on 35 completed projects and programs. The framework consists of 56 EVMS maturity attributes that make up ten EVMS subprocesses (e.g., organizing, planning and scheduling, risk management, etc.) with each allowing measurement in terms of five maturity levels. These attributes are core characteristics or qualities of subprocesses that are essential to fielding an effective EVMS. Organizations can use the framework presented here to assess and/or “tailor” the EVMS applied to their projects and programs to support the needs for an integrated project and program management. Out of 35 projects and programs studied, those with higher EVMS maturity showed 54% less cost overruns and 19% less change orders versus baseline on average, than those with lower EVMS maturity. Projects with higher EVMS maturity were found to achieve better customer satisfaction, business objectives and were significantly more compliant with EVMS standards and guidelines. Owners and contractors can use the framework on large and complex projects to screen EVMS application and highlight specific gaps to achieve best-in-class EVMS leading to better performance.

Implementation of appropriate management practices before commencing construction is vital to enhance construction productivity. This study aims to identify and prioritize preconstruction phase management practices (PCMPs) that can improve labor productivity in multistory building projects. Additionally, the objective of this research is to develop a stochastic model for predicting labor productivity based on the level of implementation of PCMPs. A thorough review of the literature was conducted to identify the practices that can increase construction productivity. Thereafter, questionnaires were distributed to principal contractors, and quantitative data were collected. After analyzing the data, the PCMPs were prioritized and a probability-based labor productivity prediction model was developed. The findings of the study revealed that well-defined scopes of work, short-interval plans, and buildability review are the top three PCMPs that can enhance labor productivity in multistory building projects. Correlation analysis showed that the relationship between labor productivity and PCMPs is statistically significant. The research showed that the relative importance of PCMPs varies based on project type. This study contributes to the body of knowledge by developing a novel tool to assess the levels of implementation of PCMPs and by building an innovative stochastic model for estimating labor productivity. Contractors can implement the practices identified in this research to augment the labor productivity of building projects, and use the model and a scoring tool to predict the labor productivity of multistory building projects.

Concern for the environment among individuals, stakeholders, and governments has steadily increased during the last few decades. Strict environmental regulations and policies have prompted enterprises to develop positive improvements such as green supply chain selection practices. Selecting a suitable supplier is essential for the competitiveness and profitability of an enterprise due to environmental and economic impacts. Managers face challenges as a result of uncertainty in supply chain management. This research narrows the knowledge gap by applying a decision-making approach in a fuzzy environment to consider the uncertainties for green supplier selection in the construction industry. In this paper, a new approach using the best-worst method (BWM) and the superiority and inferiority ranking (SIR) is applied to assist managers in the appropriate supplier selection, in which the assessment is carried out with probabilistic dual hesitant fuzzy sets (PDHFS). The BWM with PDHFSs is used to generate the weights for criteria and experts. Also, a consistency index based on an input-based consistency ratio (CR) is introduced to solve the problem of poor consistency and make the computing process simpler. Moreover, the extended SIR method with the PDHFS is established using the criteria weights obtained by the BWM-PDHFS. A numerical study is employed to show the reliability and validity of the proposed approach, and comparisons are made to highlight its superiority.

Work-related musculoskeletal disorders (WMSDs) are the main causes of physical diseases of workers in the construction industry. The occurrence of WMSDs can result in project delays, cost overruns, workers’ worsened health status, and even unanticipated safety risks. Ergonomic rules and instrument-based methods have enabled automatic postural ergonomic evaluation and intervention, while a research gap lies in how to consider more comprehensive personalized factors and how to integrate individual risks for project-level risk control. This study developed a two-hierarchy method to assess the ergonomic risk for both individual workers and the entire project. To alleviate the problems of data insufficiency and imprecision in real-life construction projects, experts’ evaluations were incorporated with existing instrument-based methods. A methodology combining fuzzy theory, D-S evidence theory, and Bayesian network was employed to deal with fuzziness, uncertainty, and conflicting judgments, and to provide a probabilistic assessment. To measure the potential impact on projects from the perspectives both of cost and productivity, this study also developed a practical framework for project integration. A real-life case study was conducted to validate the approach and demonstrated its applicability. The results of sensitivity analysis indicate that previous WMSD records, age, working posture, and working intensity are the top critical risk factors that have the most significant impact on individuals’ WMSD risk status. This study provides deeper insights into WMSD risk assessment, which can facilitate personalized instructions and intervention for individual WMSD risk control, and support managerial initiatives and decisions to mitigate the negative effects on construction projects.

Abstract

Building information modeling (BIM) is becoming a comprehensive, collaborative process in the architecture, engineering, and construction (AEC) industry. BIM’s purpose is collaboration between project participants while integrating information and completing successful projects. Persistent and recognizable names are used as direct element identifiers in BIM. Currently, element names are manually keyed in by participants. Common data environments (CDE) are recommended by BIM standards for managing information. We discuss the collaborative environment framework in construction. Participants retrieve data using element names in data environments. To reduce manually naming BIM elements, we propose an approach that ensures a unique, recognizable name. The approach strategy is summarized in four points: (1) geometric forms are separated from geometric properties, (2) list data structures are utilized to store naming conventions, creating element names to link databases, (3) project data are distributed to participants in centralized and distributed data environments, and (4) the Grasshopper program is used to create unique element names, which are stored in a database. We present a case study of a physical curtain wall project for verification. A discussion and analysis of the five procedures, as well as the advantages and disadvantages, of our approach in diverse collaboration environments are explored.

Practical Applications

BIM data are more and more comprehensive and valuable in a construction project. A lot of worker power is required to generate bills of material (BOM) before elements’ names are keyed in by participants, as well as the same needs for assembly, logistics, and scheduling. The importance of functional element (FE) element naming is to reduce manual work for producing bills of materials in our case study. Therefore, project participants can get the full value of using BIM from designing through to fabrication and installation, and identify every manufactured, prefabricated element and assembled product in the logistics process. Through our proposed approach, project participants can reduce a lot of worker power in naming BIM elements and storing BIM data to a database. This FE naming also helps project participants to quantify and connect all necessary information, increasing the overall output of BOM lists, logistics, and scheduling. Coding is carried out on the parametric model, and the coding information on the model can be reflected into the bill of materials at the same time. All the information is related to each other, creating more spare time for checking and analyzing how to effectively arrange the scheduling process.

Claim management plays an essential role in the success of construction contracts. The enhancement of the contractual claim process in construction projects improves the relationship between parties and impacts conflicts and disputes. Previous studies related to contractual claims focused primarily on general causes with relatively little attention paid to reasons associated with reworking. Rework in construction contracts generates cost overruns and delays, resulting in claims, conflicts, and disputes. Given the causal relations of rework, contractual claims, and disputes in construction projects, the necessity to implement rework management has become more critical. Although construction reworks lead to claims and disputes, a quantitative approach has not been undertaken to analyze the relationship between rework causes and contractual claims. This study aims to identify the key causes of rework that affect construction contracts and categorize them as rework factors in generating contractual claims by apprising the correlative rework causes and contractual claims. A quantitative approach is considered an appropriate method for data collection. Based on a review of the literature, a questionnaire including a comprehensive list of rework causes was developed, and the collected data were then analyzed using the exploratory factor analysis method. The results provided evidence of categorizing the causes of rework under four underlying factors: process, management and planning, material and equipment, and human resources. This study bridges the knowledge gap of dimensionality between rework and contractual claims, which can create measures to address rework causes in generating contractual claims. The paper suggests that effective rework management could reduce contractual claims through attention to the causes of rework at the early stages of contracting.

Prefabricated construction has been widely recognized as a primary method for sustainable construction, improving workflow continuity, increasing resource consumption efficiencies, and minimizing construction waste. While the benefits have been explored, its adoption constraints remain unexplored. Very few studies investigated the complex interrelationships between cost risk factors. The literature lacks a methodology that does not fall short of considering these interrelationships. Due to various interactive constraints, the development of prefabricated construction remains in its infancy. Therefore, the study aims to conduct cost risk identification and assessment methodology to investigate the interrelationships among barriers. In achieving this endeavor, a literature review was conducted first, followed by questionnaire surveys. Lastly, a risk assessment tool was developed by leveraging simulation models of Bayesian networks. The results showed that machinery and technology, direct costs, scheduling and planning, safety hazards, and site layout are the top five significant factors in implementing prefabricated construction with an assessment of 0.817, 0.777, 0.711, 0.687, and 0.666, respectively. The attention is increasing on the economic, social, and environmental dimensions of sustainability. The Bayesian model shows that the three most influencing risk factors are a lack of machinery and technology, inadequate experience, and modifications to existing policies that directly influence at least three factors. The three most influenced risk factors are poor understanding, safety hazards, and scheduling and planning which are directly influenced by at least three factors. The study proposed the following mitigation strategies: (1) apply control measures for critical cost risks, (2) control influencing risks, and lastly (3) monitor highly influenced risks. The uniqueness of the study methodology is that it adopts a simulation model that not only simulates the interrelations of the factors but also updates the model when new evidence is revealed. Hence, when conducting a risk control and risk reassessment, the model will provide a prescriptive analysis to proactively control risks. The research builds upon existing knowledge and bridges a gap by investigating cost risks and their interrelationships. Study findings are informative to policymakers, providing a better grasp on prefabricated construction.

The percent plan complete (PPC) is a crucial performance metric for a last planner system (LPS). The high positive correlations of the PPC with time and cost performance enable a project team to obtain long-term projections from microachievements. Because predicting PPCs is helpful for project control, this study aimed to investigate the temporal nature of PPCs and develop a time series modeling framework for PPC forecasting based on historical PPCs and the reasons for noncompletion (RNCs). This study found that, although PPCs and RNCs are captured weekly, their impacts on future performance can spread over a longer time span, and future PPCs can be predicted based on historical values. A minimum data time frame of 18 weeks was proposed in the context of the case project. Historical RNCs also impact PPC forecasting. The inclusion of key RNCs can help improve the forecasting accuracy. The findings from this study provide an insight to the hidden temporal nature of the PPC metric resulting from the practical implementation of the LPS. This model can be used as a prediction tool, allowing project teams to anticipate project outcomes and design suitable execution strategies.

Abstract

Reinforced concrete structures are an inherent part of the construction sector. Every in situ reinforced concrete structure cardinally involves rebar-fixing works, which entails activities like transporting, marking, laying, and tying the rebar for the components under construction. Rebar-fixing absorbs a significant number of man-hours during the operation. Therefore, productivity of rebar fixers is a critical concern in construction. Several attributes influence rebar-fixing productivity. Former studies have investigated the impact of the attributes separately and predominantly using qualitative data. A few studies have assessed rebar productivity quantitatively but ignored the effects of some crucial parameters, such as congestion, crew composition, and construction delays. Moreover, studies are limited to account for all time proportions like productive, semiproductive, and nonproductive while assessing rebar productivity. Therefore, this study addresses the aforementioned research gaps by assessing the impact of diverse attributes using direct measures of rebar-fixing productivity and its attributes. Initially, hypotheses were developed to determine the influence of eight attributes identified in this study on rebar-fixing productivity. Next, the data were recorded from one large construction project in Gujarat, India. The work sampling approach was adopted for the productivity assessment of rebar fixers at the project site to evaluate each time proportion. Furthermore, the stepwise regression analysis was performed on the data set to examine the hypotheses. Results indicate that attributes such as quantity of reinforcement, characteristic rebar diameter, and congestion have a significant positive impact on rebar-fixing productivity, whereas work duration has a significant negative impact on it. The outcomes of this research can aid the site management, structural engineers, and construction planners in decision-making to enhance the productivity of rebar fixers, thereby adhering to the budget and schedule constraints of the project.

Practical Applications

Reinforcement fixing is a major activity that significantly controls the revenue and duration of any construction project. Labor is an indispensable part of this activity. Therefore, it is crucial to guard the output of the rebar fixers. This study is a guiding light for the construction stakeholders to manage the productivity of rebar fixers. The various identified attributes are practically examined to know their impact on the productivity of rebar fixers. The results can be helpful in decision-making and process improvement at the site management level. During the designing stage of a structure, it can support the design backed up with evidence to improve the rebar-fixing productivity significantly.

The difficulty in financing has become the primary obstacle in the process of promoting a public–private partnership (PPP). Asset securitization is a financial model that can exchange the predictable cash flow of illiquid PPP project assets with the current cash flow, and credit enhancement is crucial in this process. Different from the traditional external credit enhancement of PPP projects based on government guarantees, this paper introduces an innovative credit risk management tool, i.e., credit default swaps (CDS), into the process of asset securitization in the PPP model. In order to avoid opportunistic behavior caused by information asymmetry in the process of financial product transaction, a CDS trading contract between PPP project investors and risk management institutions based on the principal–agent theory and a flexible CDS pricing model is proposed. Through a demonstration case of the Leshan to Rongjiang Expressway in China, the volatility correlation among CDS contract default probability, pricing, and related factors, such as default limits, operation period, compensation amount, project investment return, investment return volatility, and PPP project investors’ expected inputs, is obtained. The study shows that (1) appropriate CDS pricing helps to generate investors’ high expected inputs and diligence with controls; (2) there is a positive correlation between the change of default limit and compensation amount, and the set value of default limit should not be too high, otherwise it may induce the increase of default probability; and (3) based on the high return on assets and moderate asset volatility of PPP projects, the probability of default of CDS contracts is low. The innovative financial mechanism developed by the principal–agent theory could enrich the credit enhancement methods in asset securities of PPP projects.