Journal of Construction Engineering and Management

This study develops a quantification proposal for those quality costs that construction companies cannot estimate through objective criteria, known as hidden quality costs. This proposal will later be the object of validation through a real case: the quantification of the cost of loss of image that a construction company experiences as a consequence of a delay in the completion of a certain building. This proposal is of relevance to researchers in that it demonstrates how hidden cost quantification based on a fuzzy logic model can be developed and tested, and it provides the basis for future investigations. It is of relevance to professionals as it describes how fuzzy logic and quality costs can be used to effectively model industrial construction quality management.

Policies regarding public building construction affect the interests of taxpayers, construction authorities, general contractors, specialty contractors, and other stakeholders. At the state level, the debate as to the optimal form such policies should take has become an ongoing struggle among competing interests. This study presents a systematic analysis of the main issues regarding single versus multiple prime contracting with the purpose of providing objective data to illuminate the debate. A statistical analysis of project bids and final costs from a national sample of state construction projects reveals that public construction projects organized with multiple prime contracts tend to have 5% less direct costs than projects using a single prime contractor. Moreover, approximately 80% of these savings are attributable to lower bid costs for multiple prime projects. The results of this study are in agreement with theoretical bidding models and efficient risk allocation models. Theoretical bidding models suggest that, in the absence of disruptions, multiple prime projects should have lower direct costs than single prime jobs. Efficient cost allocation models suggest that when specialty contractors do not bear the risks associated with the single prime contracting method (e.g., bid shopping and payment delays) they are willing to lower their bids, and forego the premium they would normally charge in response to such risks, as seems to be the case in multiple prime jobs.

This research is based on the premise that there is heterogeneity in the population of contractors, i.e., that individual contractors exhibit different bidding behavior when confronted with a given set of bidding variables. Random-coefficients logistic model is used to explicitly measure the heterogeneity across contractors in terms of their (1) intrinsic bid/no-bid preferences and (2) responses to four bidding variables, i.e., number of bidders, market conditions, project type, and size. The binary bid/no-bid decisions were assumed to arise from a logistic model, but with the model parameters that varied between contractors. Data were gathered using a bidding experiment involving Hong Kong and Singapore contractors. The results show that there is significant heterogeneity across the contractors in which Hong Kong contractors can be clustered into four groups of identical (statistical) behavior in response to the four bidding variables, whereas in Singapore, there are only three groups as determined by the underlying heterogeneity distribution. This probabilistic classification of contractors has implications for contractors’ competitive strategies by targeting different groups of competitors toward maximum competitiveness.

In recent years more and more construction projects used three-dimensional/four-dimensional (3D/4D) models to support management tasks. However, project managers still struggle with evaluating how the 3D/4D model technology can be most efficiently applied on their specific project. One main reason for this struggle is that an account about how 3D/4D models have been used in the past is missing. This paper offers practitioners and researchers such an account of the application areas of 3D/4D model technologies including the purposes for which these technologies have been applied. The paper qualitatively aggregates the results of 26 case studies of 3D/4D model applications on construction projects to show researchers and practitioners how 3D/4D models have been applied to address project challenges. Using a “project challenge—3D/4D model application” matrix the paper explains each application area and describes why the application has been beneficial to the case study projects. The paper then analyzes the challenges that practitioners have faced with 3D/4D models on the test case projects. The main findings of this analysis are that practitioners on most of the test case projects have used the models for only one application area. The paper suggests that further research on the integration of 3D/4D model technologies into work and business processes of project teams is needed to address this opportunity for a more widespread use of 3D/4D models throughout the lifecycle of a project.

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.

The construction industry relies on skilled craft workers supplied through various training programs. Based on a nationwide survey of industry experts regarding craft training programs, this paper examines the current state and effectiveness of construction craft training via 93 respondents from a wide range of U.S. industrial and commercial construction firms and associated craft training centers. The study’s survey data allowed the writers to examine the major issues regarding craft training, such as the relative importance of basic core topics, training completion rates, the relation between trades’ engagement and training, and the barriers of advancing craft training in construction. The paper also uses the experts’ responses to estimate the benefits of craft training on productivity, turnover, absenteeism, safety, and rework. Various statistical methods are used to support the analysis. Problems are identified in craft training among the training programs examined and potential solutions for improving craft training effectiveness are suggested.

Engineering and construction projects are dependent on two fundamental elements: (1) the ability to plan and manage the technical components of the project such as the tasks and resources; and (2) the ability of the project participants to effectively develop into a high performance team. Historically, the industry has focused extensively on optimizing the project management processes associated with the former element. In this focus, organizations have emphasized the ability to develop the optimum plan, allocate resources efficiently, and utilize control functions to ensure that the project stays on schedule and within budget. Although this has been effective, this engineering focus has reached the point of diminishing results. Specifically, the engineering approach to project management has neglected to recognize the importance of the participants to the success of the overall project. Rather, the engineering approach has favored the development of an optimum plan as the path to effective project management. In this paper, the engineering-based approach to project success is reconfigured to reemphasize the need to develop high performing teams by recognizing the importance of the project network. This recognition is formalized in the social network model of construction that integrates classic project management concepts with social science variables to enhance the focus on knowledge sharing as the foundation for achieving high performance teams and project results.

The earned value management (EVM) method is considered as a powerful tool that supports the management of project scope, time, and cost. However, conventional EVM may cause some project managers to ignore the information that it can provide about the performance variations of their projects as well as their causes and effects. This paper attempts to refine and improve the performance of traditional EVM by the introduction of statistical control chart techniques. Individual control charts are used as tools to monitor project performance data so that adverse changes can be detected in a timely manner. This allows analysis of in-progress project cost and schedule performance variations and highlights possible needs for corrective action. A comparative analysis between traditional EVM and the new approach is undertaken to identify the effectiveness of the new approach.

Environmental restoration is a matter of national concern. Decades of abuse by industry, agriculture, and the military have caused devastating contamination of the earth, air, and water. The Department of Energy alone will spend hundreds of billions of dollars on containment and restoration. It is imperative that restoration costs are minimized. Every dollar spent on restoration is a dollar that will not go toward research, a dollar that will not go to upgrade our nation’s infrastructure. The work presented here uses cost as a decision variable in restoration projects. Contaminated sites frequently vary from one point to another in type and level of contamination. In addition, a single piece of property may contain several distinct contaminated areas, each of which has characteristics unlike any of the other areas. Thus one should look at optimizing the selection of remediation technologies to address the variation. A methodology has been developed that will optimize the selection of remediation technologies based on cost. This methodology uses geostatistics and dynamic programming to break a site into discrete cells and then select the optimal sequence of remediation technologies.