Journal of Construction Engineering and Management

Firms are connected with other firms through a variety of economic relationships that can be viewed as a form of social network. Resource-dependency theory (RDT) suggests that formal joint ventures among firms occur as a result of resource procurement, perceived strategic interdependence, expansion opportunities, legitimacy, and risk mitigation. A key unexplored element of RDT is the collaborative structure among firms with technical capabilities subordinate to their alliance partners and whether such structures are able to leverage sufficient market power to influence the industry sector within which they exist. Using the network characteristics of alliance partners in a technology- and capital-intensive industry sector, this work empirically examines the aggregate market power of so-called generalist firms. Collaborative patterns in the resources and mining sector and their impact on firm performance and resource quality are investigated using social network analysis. It is found that firms engaged in strategic alliances outperform firms operating independently; however, beyond a certain number of alliance partners, their performance declines. It was also found that, in aggregate, generalist nontechnical alliance partners can exercise significant market power in dense alliance networks, despite possessing almost no technical industry experience.

Improper design in the construction industry leads to change orders, rework, decreased constructability, cost overruns, and delays, making it one of the biggest causes of waste. The writers aim to develop a lean design process to enhance design reliability by creating a learning environment using the design correctness ratio. Waste is first identified by analyzing the planning and design processes. A new design workflow is then proposed using lean concepts to smooth design work, reduce unnecessary design errors, and increase design reliability. The proposed process can provide team members with feedback on design status and thus allow for continuous improvement. The lean process is conceptualized using system dynamics to validate applicability. Analysis shows that the proposed lean design process can enhance design completeness and reliability, thus increasing design correctness. Waste due to improper design could be reduced accordingly. The proposed process is one of the first to apply the lean approach to construction-project design.

Modularization is a well-established technique that can improve the construction industry by exporting a portion of site-based work to fabrication shops. However, its application in the industry is limited. The Engineering, Procurement and Construction (EPC) industry needs new approaches to reach higher levels of modularization. The purpose of this study is to determine what changes in current EPC processes are needed to create an optimal environment for a broader and more effective use of modularization. In creating such an nvironment, it is necessary to know the factors that lead to success. The most influential critical success factors (CSFs; 21 total) have been determined by the expertise of a research team who compiled a detailed list by adding CSF enablers. CSF enablers are additional steps that can facilitate accomplishment of the associated CSF. A closer look at the top five CSFs indicates that project teams should pay particular attention to module envelope limitations, team agreement on project drivers, adequate owner-planning resources and processes, timely freeze of scoping and design, and due recognition of possible early completion from modularization. This CSFs analysis and addition of enablers provide guidelines and ways to implement strategies for successful modularization in a project and leverages modularization to benefit capital projects.

Although they are the safest means of transporting oil and gas products, pipelines can sometimes fail with hazardous consequences and large business losses. The decision to replace, repair, or rehabilitate depends mainly on the condition of the pipeline. Assessing and predicting its condition is therefore a key step in the maintenance plan of a pipeline. Several models have recently been developed to predict pipeline failures and conditions. However, most of these models were limited to the use of corrosion as the sole factor to assess the condition of pipelines. The objective of this paper is to develop models that assess and predict the condition of oil and gas pipelines based on several factors including corrosion. The regression analysis technique was used to develop the condition prediction models based on historical inspection data of three existing pipelines in Qatar. In addition, a condition assessment scale for pipelines was built based on expert opinion. The models were able to satisfactorily predict pipeline condition with an average percent validity above 96% when applied to the validation data set. The models are expected to help decision makers assess and predict the condition of existing oil and gas pipelines and hence prioritize their inspection and rehabilitation planning.

Indirect construction costs (IDCC) comprise from as little as 10 to 40% or more of the total cost of construction projects, in part depending on the type and nature of the project. Yet, any location in this range represents a significant component of total project costs, which justifies the need to better understand and develop effective practices to estimate, control, and manage IDCC. When IDCC practices are not afforded intentional management and appropriate resources, key project performance outcomes are affected. As its unique contribution to the body of knowledge, this paper operationally defines IDCC, outlines certain innovative IDCC practices, and identifies statistically significant relationships between key IDCC practices and project outcome metrics. Empirical recommendations are premised upon data collected through testimonials, structured interviews, and brainstorming sessions of an extensive set of construction industry professionals. Statistical findings are based upon data collected through 56 one-on-one data-collection interviews with subject matter experts and 47 project-level surveys. Qualitative data were filtered, organized, and aggregated and are technically presented as instructional guidelines. Quantitative data were statistically analyzed and results from this analysis show several significant relationships between IDCC practices and key project outcomes, including a positive correlation between the frequency of IDCC review meetings that included project managers and field supervisors and quality and cost performance.

The accuracy of cost estimates is crucial to the success of construction projects with knowledge of project location being fundamental to the outcome of cost estimating. Location factors are commonly used to adjust construction cost estimates by project location, but these factors are not available for all locations. To date, the nearest neighbor (NN) method, based on simple, proximity-based interpolation has been used when a city lacks a sampled location factor even if this method was not empirically validated. This paper provides several contributions. First, the validity of the proximity-based method was substantially supported through the analysis of the global spatial autocorrelation of the changes in location factors from 2005 to 2009. Using two established cost index databases, it was found that there is spatial correlation between cost indexes for specific cities. Therefore, cost estimators can spatially interpolate values to understand cost indexes at places that are not listed in the database. Second, comparing different spatial interpolation techniques, including NN for how well they can interpolate cost indexes across space revealed that two alternative methods, the conditional nearest neighbor (CNN) and the inverse distance weighted (IDW) methods, lead to more accurate estimates. These findings are relevant to industry practitioners because they justify the use of interpolation as well as identify how to obtain improvements in preliminary estimates by simply replacing an interpolation method with another. Last, this study also provided preliminary evidence supporting the development of spatial prediction models for construction costs, which is an additional point of departure from the existing body of knowledge.

Due to the inherent nature of the construction industry, all construction projects have some amount and type of uncertainty. This uncertainty can lead to variation in project task durations from what was originally planned. As a result, construction personnel compensate for the uncertainty by adding a time buffer to absorb variations in the work plan. For the purpose of this research, a time buffer is defined as the amount of time included in task durations to compensate for uncertainty and to protect against variation. Variation is defined as the difference between the planned and actual task duration. Although previous research acknowledges time buffer additions, the root causes for adding a buffer have not been thoroughly researched. A nationwide survey was administered to project managers, superintendents, and foremen to identify the most severe reasons for adding time buffers to construction task durations. The root causes of variation have been previously identified and are used as the comparison benchmark. Contributions to the body of knowledge include (1) comparing and contrasting the overall most severe causes of time buffers with the most severe causes of duration variation to identify disconnects between the perception of concerns about uncertainty and the reality of what causes task duration variations at different levels of management; and (2) the use of factor analysis to quantitatively identify the underlying structure of time buffer factors. The findings of this research will help construction managers understand what drives the need for buffers in their construction schedules and allow them to focus efforts on strategically addressing the most critical areas of concern and uncertainty. Furthermore, the research reveals similarities and disconnects in the effects of uncertainty on planning and task execution.

Interactionism posits that all social structures have inherent conflicts within them that can yield positive or negative effects on organizational performance. Based on this theory, three essential types of conflict between the owners and contractors in construction projects were identified, namely, (1) relationship-related conflict, (2) process-related conflict, and (3) task-related conflict. Next, the authors of this paper presented four hypotheses to test the correlations among these three conflict types as well as influencing relationships between these conflicts and project cost performance. A total of 209 managers from the Chinese construction industry were interviewed to test these hypotheses. Factor analysis, correlation analysis, and regression analysis were performed on the data, revealing that (1) the three types of conflict are all positively correlated, (2) the relationship-related conflict has a negative impact on project cost performance, and (3) the task-related conflict influences project cost performance in an inverse-U shaped manner. These results can help conflict managers better understand the relationship between conflict and project performance, and encourage them to optimize different types of conflict for better project outcomes.

Transportation agencies worldwide have been preparing themselves to face the growing demand for transportation infrastructure by forming public-private partnerships (PPP), contractual agreements formed between public and private sector entities, to allow for greater private sector participation in the delivery of transportation projects. However, there is one major concern that needs to be addressed: the issue of accurately estimating maintenance and rehabilitation costs at the project planning stage. The accuracy of these estimates plays a significant role in determining project characteristics, and in selecting appropriate projects and PPP approaches for implementation, so that they may lead to sustainable highway preservation strategies. This paper compares the contract winning bid cost with the final as-built cost of highway maintenance and rehabilitation projects, determines possible cost overruns across the projects and identifies influential factors that affect them. The analysis is performed separately by PPP contracting approach, and thereby relaxes the assumption of past research that cost overrun behaves identically across different contracting approaches. The results show that a number of factors play a role in the determination of cost overrun, including the project size (cost, duration, and length) and specific maintenance and rehabilitation activities.

Although the importance of energy efficiency investments has grown substantially in recent years, there has been very little about reliable valuation methods specifically aimed for commercial building energy retrofits under building performance risks. To fill this gap, the key objective of this study is to develop and exemplify a real options valuation framework that can be used to evaluate the financial impact of phased investments in a building portfolio. This study proposes a rigorous theoretical decision-support framework: Phased Investment for Energy Retrofits (PIER). PIER blends real options and net present value (NPV) analysis techniques together to valuate managerial flexibility associated with the phased investment strategy. The research results revealed that use of PIER would lead to a favorable NPV while performance risks could serve as a crucial factor that determines the level of government subsidy. The valuation framework of this study will help project owners and decision-makers better assess the impact of performance risks on commercial energy retrofit investments, which will serve as a solid foundation for them to make further better-informed project decisions.

This paper explores the influence of cash-flow correlations on the behavior of the net present value (NPV) and internal rate of return (IRR) when performing valuations. In general, correlation has a negligible effect on the expected value of both the NPV and IRR. Even in cases of high correlation the IRR distribution departs very little from normality. In cases of moderate to low correlation, very good approximations of the SD of the NPV and IRR can be obtained, assuming that the cash flows are independent. These results, coupled with the simple two-parameter correlation structure investigated, provide a useful framework to perform valuation analysis of large-scale civil engineering projects.

In ready mixed concrete (RMC) dispatching, common practice is to rely on human experts for any necessary real-time decision making. This is because of the perceived complexity of RMC dispatching and the general lack of highly applicable optimization tools for the task. Critically, the accuracy of expert decisions (compared to optimization approaches) has not been comprehensively examined in the literature. To address the question of current practice expert accuracy in the context of optimized outcomes, this paper first mathematically models the RMC dispatching problem according to methods introduced in the literature. Two approaches are taken: integer programming (without time windows) and mixed-integer programming (with time windows). Further, the constructed models are tested with field data and compared to the decisions made by experts. The RMC data set has four depots and approximately 40 trucks that typically supply 40–200 deliveries per day. The results show that, on average, experts’ decisions are 90% accurate compared to the optimization models under examination. Finally, further investigation suggests that at least a portion of this optimality gap between expert and optimization models occurs because experts in critical situations accept a higher cost to ensure a more stable dispatching system.