Journal of Construction Engineering and Management

This article examines how risk is reflected in infrastructure regulatory contracts, using examples from water utilities to illustrate key points. Partnerships between public and private sectors in intensive capital network services require risks to be assigned to the contractual party that is better able to mitigate them or to bear them. After identifying risks that must be addressed in infrastructure contracts, their classification, allocation, and impact are presented along with the measures to minimize risks. Two contracts in the water sector in Portugal are analyzed. One arrangement corresponds to a public–private partnership (PPP) of the purely contractual type (concession arrangement) and the other to an institutionalized PPP (mixed company). We conclude that risk is a key issue in contracts with the private sector; an appropriate allocation of risks is a necessary condition for successful contracts.

Traditional approaches for cost forecasting tend to utilize a single model for the entire construction period. However, a construction project, consisting of different stages, will incur different costs, which may not be accurately captured by a single model. Gates separated the $S$-curve into three periods. Utilizing the same approach, the accuracy of cost forecasting can be improved by dividing the entire duration of a construction project into three periods. Therefore, this research aims at improving the traditional Grey prediction model by defining the suitable $\alpha$ instead of using 0.5. This new technique applies the golden section and bisection method to optimize $\alpha$ and build the short-interval cost-forecasting model. In each period of the construction phase, a customized optimization-forecasting model is used to estimate each short-interval cost. The proposed models should more closely predict the short-interval cost, which can be utilized to more accurately forecast the expenditure of the subsequent month within each period.

Developing a reliable project cost estimate is a challenge for any state highway agency (SHA), especially at the conceptual stage. A conceptual estimate is defined in this paper as the estimate prepared at the point at which only 30% of the design is complete. This paper describes a statistical approach to producing a reliable conceptual cost estimate when few project design details have been finalized and many assumptions still form the basis of the estimate. This approach used an analysis similar to the program evaluation and review technique (PERT), which is more commonly used in project scheduling, to assign certainty factors to cost estimates. The approach uses a combination of historical bid data for major roadway items whose quantities can be estimated early in the development process and historical percentages for other major components of the project, called allowance and contingency factors. The paper focuses on (1) the methodology developed to analyze the historical bid data; (2) the analysis of 14 highway corridors with 77 projects whose as-bid construction costs were more than $830 million; and (3) a cross-validation of the approach used to validate the accuracy of the predictive model. By using a PERT-type technique, construction costs were accurately predicted at the conceptual stage within $\pm 20\%$. However, approximately 85% of the corridor costs were accurately predicted within $\pm 15\%$ of the actual cost. The proposed methodology provides a structured and consistent estimating approach that can be used by any SHA that needs to develop total project delivery estimates at the conceptual design stage.

Concurrent engineering is the process of reducing the total project delivery time by overlapping activities that normally occur sequentially. The benefits are a shortening of delivery time and a possible reduction in overall costs. However, there are potential risks to overlapping dependent activities. The interaction between an upstream activity and a downstream activity relies on how information evolves in the upstream activity and how sensitive the downstream activity is to that changing information. This implies that if an upstream activity is slow to evolve information and a downstream activity is highly sensitive to changing information, the risk in overlapping these activities will be higher than activities that are fast to evolve and not as sensitive to changing information. The risk can manifest in rework, or reperforming some aspect of the downstream activity because of the evolution of the upstream activity and the amount of overlapping used. This research developed computer simulation models to better understand the potential risk of rework. The simulation models allow for overlapping different types of slow- and fast-evolving upstream activities with high- and low-sensitivity downstream activities using overdesign and early release strategies and different amounts of overlapping. The overall findings show that simulations of overlapping activities yield a reliable approach to modeling concurrent engineering activities. The simulations provide information on the probability of rework based on the different combinations of evolution, sensitivity, overlapping strategy, and the percent amount of overlapping used between the upstream and downstream activity. The use of computer simulations and modeling of overlapping activities therefore allows engineers and managers to understand the potential risks and what strategies to apply when utilizing concurrent engineering on a project.

Reliable cost estimates are essential for effective project control and the management of cash flows within the project and at the company level. Conventional approaches to project cost forecasting, which rely on detailed information developed for a specific project (the bottom-up estimate or inside view), often result in cost overruns. It is argued here that the inside-view project cost estimates should be adjusted by combining them with the outside (or top-down) view of the project, which is based on statistical models of historical project data. This paper presents a probabilistic cost forecasting method and a framework for an adaptive combination of the inside view and the outside view forecasts of project cost using Bayesian inference and the Bayesian model averaging technique. During the project execution phase, the Bayesian adaptive forecasting method incorporates into the predictions the actual performance data from earned value management and revises preproject cost estimates, making full use of the available information. Qualitative examples are presented to demonstrate the validity of the proposed method as a tool for effective project cost prediction and control.

Many existing resource-constrained scheduling tools may be inadequate for representing and evaluating intermediate functions, a major category of construction requirements, with respect to availability time-windows and alternative construction sequences. This study develops a time-window analysis approach for calculating and matching the requirement and availability time-windows of an intermediate function. Consequently, the proposed analysis approach can facilitate planning engineers to formally detect unfulfilled construction requirements residing in a construction schedule. Moreover, the analysis tool can also be used for accommodating alternative construction sequences to work out better construction schedules, which is demonstrated and validated by using the case of constructing a nursing house.

The accumulation of material buffers is commonly perceived within the construction industry as an effective means of shielding a project from the risks associated with uncertainty in the supply network. Much of the uncertainty arises out of a lack of visibility throughout the construction supply network, in which visibility refers to the level of awareness of the overall state of the supply network. The integration of Automated Materials Locating and Tracking Technologies (AMLTT) within the construction supply network presents a viable solution to this problem. This article presents the results of an investigation that examined the potential for AMLTT to increase work opportunities at the site level as a result of increased supply-network visibility and in turn reduce the dependency on material buffers. The investigation was completed by using a modeling and simulation approach grounded on a solid foundation of field data and experience. The results presented here are increasingly important as leaders in other industry sectors are beginning to report tangible benefits as a result of increased supply-network visibility as a result of the integration of AMLTT within their organizations’ supply networks.

Preventing injuries to workers is essential to effective organizational management in the construction industry. It is generally accepted that achieving a high level of safety performance requires the implementation of an effective safety program. In an effort to provide guidance for the development of an effective safety program, previous studies have focused on identifying highly effective injury prevention strategies and quantifying their individual impacts on safety performance. Despite these advancements in knowledge, the synergistic interrelationships among highly effective injury prevention strategies remain unknown. The objective of this paper is to describe the results of a Delphi study that quantified the interrelationships (i.e., pairwise cross impacts) of highly effective and commonly implemented injury prevention strategies. Analysis of the data indicates that the site safety manager, worker participation and involvement, a site-specific safety plan, and upper management support and commitment are the most central elements in an effective program. It is expected that the results of this study can be used by practicing professionals when designing a new safety program, enhancing an existing program, or conducting economic analyses of safety-related investments.

Absorptive capacity is a firm’s ability to value, assimilate, and utilize new external knowledge and apply it to commercial ends. Much of the prior research on absorptive capacity focuses on characterizing the factors that influence absorptive capacity within organizations. However, the mechanism of how related factors affect absorptive capacity across interdependent organizations in project networks remains less explored. This paper extends a simulation model of project network learning to explore the absorptive capacity of project networks where periodic external innovations exist. This model is utilized in a series of simulation experiments to untangle the effects of varying types of innovation and degrees of relational instability in a project network. We establish a measure of project network absorptive capacity and develop an argument that relational instability moderates the project network’s absorptive capacity for different types of innovation. These findings have significant implications for assessing and developing strategies to improve a project organizational network’s capacity to absorb and, hence, profit from innovation.

Risk allocation plays a critical role in privately financed public infrastructure projects. Project performance is contingent on whether the adopted risk-allocation strategy can lead to efficient risk management. Founded primarily on the transaction cost economics, a theoretical framework was recently developed to model the risk allocation decision-making process in privately financed public infrastructure projects. In this paper, a neuro-fuzzy model adapted from an adaptive neuro-fuzzy inference system was further designed based on the framework by combining fuzzy logic and artificial neural network techniques. Real project data were used to train and validate the neuro-fuzzy models. To evaluate the neuro-fuzzy models, multiple linear regression models and fuzzy inference systems established in previous studies were used for a systematic comparison. The neuro-fuzzy models can serve the purpose of forecasting efficient risk-allocation strategies for privately financed public infrastructure projects at a highly accurate level that multiple linear regression models and fuzzy inference systems could not achieve. This paper presents a significant contribution to the body of knowledge because the established neuro-fuzzy model for efficient risk allocation represents an innovative and successful application of neuro-fuzzy techniques. It is thus possible to accurately predict efficient risk-allocation strategies in an ever-changing business environment, which had not been achieved in previous studies.

Large quantities of greenhouse gases (GHG) are emitted in producing and acquiring materials for the construction, maintenance, and rehabilitation of highway infrastructure. The objective of this paper is to develop and illustrate a method that can be applied by state agencies to quantify the life-cycle emissions associated with different pavement designs. It applies existing life-cycle assessment (LCA) methods that integrate process-level construction data. The research emphasizes the construction phase and contributes a method that can be used to develop and analyze construction phase life-cycle inventories. It describes on-site collection of material and equipment usage data during construction and rehabilitation operations. Departing from traditional approaches that tend to use LCA as a way to compare alternative pavement materials or designs on the basis of estimated inventories, this paper proposes a shift to a context-sensitive process-based approach that uses actual observed construction data to calculate greenhouse gas emissions using a hybrid LCA. The goal is to support strategies that reduce long-term environmental impacts. A case study involving the rehabilitation of a concrete pavement was used to illustrate the proposed method. The key findings were as follows: total ${\mathrm{CO}}_2$ emissions are 787.19 and 1,383.28 MT per lane mile for Hybrid Models 1 and 2, respectively; the production of the materials, equipment, and fuel used to construct the project account for 90% and 94% of the total ${\mathrm{CO}}_2$ emissions throughout the construction phase for Hybrid Models 1 and 2, respectively; the equipment use and transportation impacts together only represent 6–10% of the total emission through the construction phase.