Journal of Construction Engineering and Management

The construction sector is known as one of the most hazardous work places worldwide. Hazard identification and its assessment are the most important tasks of safety management. Based on a review of the existing literature, this study identifies 10 hazardous trades and their 39 attributes to assess the project hazard level. Most of the existing studies assess the hazards using the probability approach, which needs reliable established data that in most cases are not well maintained. This study uses the possibility approach (fuzzy set theory) and proposes a methodology to compute the hazard index representing the hazard level of projects. This methodology is based on the judgments and perceptions of respondents, and it uses the consistent fuzzy preference relations (CFPR) method to determine the relative weights of 10 hazardous trades and their 39 attributes. Fuzzy set theory is used to determine the risk impact of attributes. Thus, the hazard index for a given attribute is determined by a combined analysis of the overall relative importance and risk impact of hazardous trades’ attributes. The proposed methodology is illustrated by examining a metro rail construction project. By empirical analyses, the proposed methodology is validated and checked for its applicability by studying the relationship between project hazard level (hazard index) and corresponding safety budget. In comparison to existing methods, the proposed method is simple, speedy, and more consistent in computing the hazard index of a project.

This paper discusses selection of construction projects based on multiple criteria using a quantitative fuzzy-set method, which considers multiple attributes in a decision-making process involving a group of decision makers. This approach implements a modified fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to help decision makers rank projects based on various attributes. A triangular fuzzy set models linguistic terms used to provide subjective judgment related to decision makers’ experience level, attribute weight assessment, and attribute rating. The fuzzy TOPSIS method is extended in this research to capture various levels of experience for decision makers involved in a decision to select a construction project. An illustrative example of project selection using the proposed method is provided to demonstrate its effectiveness. The computational results of this research provide project stakeholders with a computer model that could be implemented to assist in the construction project bid or not bid decision and provide a ranking of different projects to bid on. Furthermore, the results of this study show that the proposed method is suitable for modeling the uncertainty associated with project selection. The bid/no bid project selection model presented in this study is based on a set of common attributes; the selection of attributes that contribute toward the project bid or not bid decision can be different based on construction project conditions. The proposed method is suitable for construction type projects, but the methodology used in the study can be implemented for any type of project and is not limited to a specific geographic area. The proposed computer model is limited to handling input from up to 10 decision makers with varying degrees of experience. Another limitation of the computer model introduced in this study is that contractors can consider up to five different projects for bid purposes.

Producing asphalt mixtures for pavement construction is an energy intensive process accompanied with enormous costs and greenhouse gas (GHG) emissions. In this study, a system of integrated thermodynamic models and a software tool were developed for the estimation of energy consumption in asphalt mixture production. The theoretical models were validated through a large amount of data collected from six asphalt mixture plants. This study contributes to construction engineering and management practices by revealing the fundamental heat and mass transfer processes in asphalt mixture production. It enables different asphalt mixture (batch) plants to use the same procedure for the estimation of energy use and the associated costs and emissions. It also helps examine the sensitivity of energy consumption to different influencing factors and offers the opportunity to improve production efficiency.

The use of proper risk analysis techniques and contingency determination procedures in construction projects improves project efficiency and effectiveness. However, the uncertainty inherent in risk and the lack of sufficient related historical data on risks make it difficult to precisely assess a project’s degree of risk exposure using classical deterministic or probabilistic risk analysis techniques. This paper provides an alternative to these techniques that uses fuzzy logic and expert judgment. It proposes a fuzzy contingency determination model (FCDM) that utilizes a novel and transparent fuzzy arithmetic procedure to determine construction project contingency using the $\alpha$-cut method and the extension principle, based on t-norms. Linguistic scales, represented by fuzzy numbers, enable experts to use natural language to assess the probability and impact of risk and opportunity events instead of depending on historical data. The model expresses contingency either as confidence intervals at different levels of confidence, or as a single crisp value resulting from defuzzification. A software tool, the Fuzzy Contingency Determinator (FCD), has been developed to implement the FCDM’s fuzzy arithmetic procedure. The model is validated by comparing its results—work package and project contingencies—to those of a Monte Carlo simulation model, using actual project data. The main contributions of this paper are (1) providing a systematic, transparent, and flexible methodology to identify and assess risk and opportunity events and determine construction project contingency, using a novel and highly flexible fuzzy arithmetic procedure based on the $\alpha$-cut method and the extension principle, the latter of which uses different $t$-norms—an approach that has not been previously applied in the construction domain to determine project contingency; (2) offering an alternative to traditional deterministic and probabilistic risk analysis approaches by using expert judgment, linguistic scales, and fuzzy numbers to overcome their limitations; (3) incorporating opportunity in its assessment procedure, which has been rarely applied in other risk assessment models; and (4) implementing the fuzzy arithmetic procedure of the FCDM using a simple, flexible, and user-friendly software tool: FCD. The ability to explore the effect of different fuzzy arithmetic procedures on contingency determination provides a generalizable approach that can be applied to different cases of risk analysis.

Discrete event simulation (DES) has been widely used for scheduling and analysis of construction projects. Integration of DES with fuzzy logic enhances the capabilities of DES by capturing imprecise, subjective, and linguistically expressed knowledge in the simulation inputs using fuzzy numbers. However, available fuzzy discrete event simulation (FDES) methodologies have significant shortcomings in handling fuzzy ranking and updating of the simulation time. These limitations often result in the inaccurate estimation of the start and completion times for individual activities and the whole project. This paper presents a new approach for calculating the event times to increase the accuracy of simulation time estimation in FDES. The major contributions of this paper are in integrating DES with fuzzy logic to increase its applicability in the construction domain and increasing the accuracy of FDES results. The proposed FDES approach was implemented in a construction project scheduling example, which confirms the higher accuracy of the proposed methodology compared to existing FDES methodologies.

Construction projects are typically carried out in highly uncertain environments with the risk of cost and time overruns, and subsequent disputes between stakeholders. One of common risk factors is that most cost items of a project are dependent random variables. Thus, correlations between basic cost items need to be considered in predicting the total cost of the project. This paper intends to propose a generic copula-based Monte Carlo simulation method for prediction of construction projects’ total costs with dependent cost items. An algorithm to generate the joint probability distribution function of correlated cost items is developed and two examples are presented to demonstrate the applicability of copulas in modeling construction costs as random variables. A merit of the proposed method is that it not only can incorporate all different types of distributions in one framework, but it also captures the best dependence structure between variables. This paper finds that different dependence structures can lead to different probability distributions of total cost. It also finds that the existing goodness of fit tests can be employed in choosing the best performing copula. The paper concludes that the copula-based Monte Carlo simulation method can predict total cost of construction projects with reasonable accuracy.

Hazard recognition and the accurate perception of safety risk are fundamental to the success of any safety program. When hazards remain unrecognized, or the associated safety risk is underestimated, the likelihood of catastrophic and unexpected injuries dramatically increase. Unfortunately, recent research has found that a large number of hazards in construction remain unrecognized. Likewise, past studies have demonstrated that safety risk is widely underestimated within construction. To improve hazard recognition and the accurate perception of safety risk, employers adopt a wide variety of training programs. However, the prevalent use of ineffective and unengaging training methods have significantly impeded training efforts in construction. The purpose of this research was to assess the impact of safety training on two objective training outcomes: hazard recognition performance and safety risk perception. The research objectives were accomplished by gathering empirical data from 51 active projects in the United States. Specifically, data pertaining to the training method (i.e., high-engagement versus low-engagement training) adopted at the project level were gathered, following which the hazard recognition ability of representative workers and their safety risk perception levels were measured. The results of the study revealed that (1) compared to low-engagement training, high-engagement training is associated with higher levels of hazard recognition and safety risk perception; and (2) the effect of training on safety risk perception is mediated by hazard recognition performance. Therefore, workers representing projects that offered high-engagement training were able to identify a larger proportion of hazards, and consequently perceived that safety risk was relatively higher. The findings of this study will be useful to practicing professionals seeking to improve training delivery, hazard recognition performance, and the perception of safety risk within construction. This study represents the first formal attempt to empirically evaluate the holistic relationship between training, hazard recognition, and safety risk perception in the construction context.

Underground utility incidents, such as utility conflicts and utility strikes, result in time and cost overruns in construction projects, property damages, environmental pollution, personnel injuries, and fatalities. A main cause of recurrent utility incidents is the noncompliance with the spatial configurations between utilities and their surroundings. Utility specifications usually contain textual descriptions of the spatial configurations. However, detection of spatial defects, according to the textual descriptions, is difficult and time consuming. This deficiency is because of the lack of spatial cognition in many rule-checking systems to process massive amounts of data. This study aims to automate utility compliance checking by integrating natural language processing (NLP) and spatial reasoning. NLP algorithm translates the textual descriptions of spatial configurations into computer-processable spatial rules. Spatial reasoning executes the extracted spatial rules following a logical order in a geographical information system (GIS) to identify noncompliance. The intellectual contribution of this study is twofold. First, complex spatial rules are retrieved automatically from textual data with their hierarchies classified, which provides the inputs and indicates the sequence of rule execution in spatial reasoning. Second, semantic spatial relations are modeled on the basis of their metric and topological implications, enabling the automatic execution of multiple spatial rules. Experiments were conducted to test this framework. The average precision, recall, and combination of the two (F-measure) achieved by the NLP algorithm for extracting spatial rules are 87.88%, 79.09%, and 83.25%, respectively. In addition, the spatial reasoning mechanism also was found to be a powerful tool for compliance checking under various scenarios.

Diffusion of project partnering in the construction industry is argued to be insufficient. One possible explanation is that organizations do not intend to form project partnering. Therefore, the current research aims at developing an intention-based model according to the theory of planned behavior (TPB). Fifty-one construction-related organizations in Hong Kong participated in this research. The partial least-square approach to structural equation modeling was used to examine the TPB model. The results indicated that perceived partnering outcomes positively and significantly predicted attitudes toward the partnering behavior, which, in turn, positively and significantly predicted partnering intentions. Past experience also played a role in the intention-based model. Both significant and nonsignificant relationships help explain the nature of project partnering. The research contributes to the existing literature by taking the first step to empirically study the role of intentions in project partnering. Moreover, the study explores the application of the TPB in explaining behaviors at the organizational level. Further research and practical implications have been provided.

Different types of functional relationships have been proposed by many authors to model the cumulative progress curve or the S-curve of a project. They have mostly tried to develop equations which generate a curve having the shape of an S and then tried to mold that into the project of choice. A completely different approach has been used based on a hypothesized behavior of the progress rate of the project. This leads to the development of beta S-curves based on the beta distribution having two parameters. Although beta S-curves have been used earlier for probabilistic forecasting of estimated duration at completion, the early and the late shape parameters of the beta S-curves are explored and ways to estimate these are presented—both before and after the detailed planning have been done. When applied to data from real projects taken from published literature, it was found that the beta S-curves uniformly reported better fit compared to the logistic S-curves.

Earlier studies investigated spatial scheduling to link schedules of construction activities to workspace availability and constraints. However, previous models lacked the ability to quantify criticality in a manner similar to the critical path method. Previous spatial scheduling models did not provide seamless integration with site logistics planning of material staging and temporary facilities. This paper therefore extracts various float types that can exist in a spatial schedule. Three activity float metrics are defined using singularity functions: shift, rate, and combined floats. An algorithm is established to generate space float contours for a construction site. A validation example demonstrates the new float concepts and space float contours. This research makes two contributions to body of knowledge of construction scheduling. First, activity float metrics expand the capabilities of spatial scheduling models to assess the criticality of activities. Second, the concept of space float enables a direct integration between scheduling and other project management tasks like site layout, material logistics, and trades coordination.

Unbalanced pricing of bid line items is considered an undesirable practice by owners and may lead to rejection of the bid on the basis of nonresponsiveness to the bidding regulations. This paper presents the development of a detection model to help owners detect objectionable unbalanced bidding. The proposed model quantifies the risk afforded by deviations of the actual quantities of work from the estimated quantities of the bid line items. The proposed model also considers the impact of multiple uncertainties in line item quantities on the owner’s total project cost based on the unit prices submitted by all the bidders for the project. Historical deviations from the estimated quantities during previous construction projects are used as an indicator of future fluctuations in the estimated quantities. The proposed model allows uncertainty in the quantities of work to be incorporated into the bid evaluation stage to assess each bidder’s likelihood of being the truly lowest bidder (i.e., yielding the lowest cost to the owner). An application example, based on actual bid results submitted to the California Department of Transportation, was used to illustrate the effectiveness of the model in detecting objectionable unbalanced bids. The model is expected to provide much needed support for owners in the bid evaluation stage and to reduce the controversy around unbalanced pricing because it targets material unbalancing rather than the whole practice of unbalanced pricing.

In Bangkok, many infrastructures have been built to satisfy the needs of the rapidly developing economy, leading to much higher construction and demolition (C&D) waste than ever before. A recycling program is, therefore, needed to properly manage the waste and to avoid landfill shortages and future environmental problems. This research examines the feasibility of a construction and demolition waste recycling program in Bangkok, Thailand, using a system dynamics (SD) modeling technique. The model consists of two main elements, namely the total benefits and total costs. The simulation results show that it takes 14 years for the recycling program to be worth the investment. However, with the minimum acceptable internal rate of return (IRR) of 12% for most government projects, the recycling program must be continued until the end of Year 17 to be considered feasible. The results also show that saving in landfill charges and green image are the two key benefits in the recycling program. This could be seen from the high impact of the recycling rate and green image rate parameters on the construction and demolition waste recycling program. The developed dynamic model provides a better understanding of the construction and demolition waste situation in Bangkok, and plans for an effective waste management in the long term.

Despite the efforts of governments to promote sustainable construction practices, the decision to adopt sustainability practices largely falls upon the firms involved in construction projects. This paper presents an econometric model that describes the relationship between the level of sustainability experience a firm possesses and its characteristics and behavioral attributes. Data for this research were collected in Qatar by means of a survey as part of a study that aimed to analyze the triggers of sustainability adoption in the Qatari construction market. The presented model showed that a number of behavioral attributes of firms, besides their origin and the average size of projects they undertake, determine the level of sustainability experience these firms might have. Examples include firms’ risk-taking tendencies, their claimed competitive advantages, and their previous history of introducing changes to their modus operandi. This research adds to the body of knowledge on sustainable construction by studying its adoption from an organizational behavior perspective that focuses on factors internal to the firms. This complements existing literature that studies external triggers such as governmental policies.

In tall building construction, the adoption of the appropriate construction method for the core structure process is critical in terms of both the structural aspect of the building and the schedule estimation. This study introduces a new core structure succeeding construction method (CSSCM) for tall buildings with steel-reinforced concrete structures to address the obstacles of the existing core structure preceding construction method (CSPCM) such as limited workspace, interference between activities, difficulties in wall and slab joint construction, and similar obstacles. The study also shows the technical details and compares the results from the applications of CSPCM and CSSCM to an actual tall building project. The results revealed that this method has potential as a cost-effective construction method for structural frameworks of tall buildings where short cycle times are required. In addition, the method provided stable working environments for the steel erection including outriggers and core works. This study will assist engineers and managers in applying the CSPCM construction method appropriately to similar projects and dealing with flexible changes in building design and engineering technologies.

The pursuit of Faster Fast Track or Flash Track is deemed necessary in today’s industry for reasons such as emergency rebuilds, competitive market advantage, and regulatory compliance. Flash Tracking has been defined as a time-driven project, which by necessity requires a heightened degree of concurrency between engineering, procurement, and construction. This paper reports on Flash Track readiness assessment algorithms that were developed to enable an organization to assess its readiness to execute time-critical or Flash Track projects. The readiness assessment algorithms were developed by defining the relative importance of 47 essential Flash Track practices, grouped into 6 categories using the analytic hierarchy process (AHP). The weights of the practices and categories were determined by a 15-member industry expert panel, comprised of 15 Construction Industry Institute (CII) member companies representing owners, contractors, and engineering organizations. The algorithms were ultimately integrated into a Flash Track toolkit. In a validation exercise, representatives from 13 completed Flash Track projects used the toolkit with reference to their projects. Project representatives’ retrospective self-assessment of readiness to perform the projects on a Flash Track basis were moderately to strongly correlated ($r=0.76$) with their readiness assessment forecasted by this research’s algorithms. This research provides the first quantitative measurement suitable to assess an organization’s readiness to undertake the unique challenges of a Flash Track project.