Journal of Construction Engineering and Management

The personal energy absorber (PEA) is a critical component of a personal fall arrest system (PFAS), and it is meant to dissipate the energy generated during a fall to prevent injuries to the user. When designing PFASs, engineers need to estimate the fall distance of the user, and one of the parameters needed is the average deployment force ($F_a$) of a PEA. However, currently there is a lack of empirical information on $F_a$. The guidance for the estimation of $F_a$ stipulated in the North American standards Z259.16 and Z359.6 did not provide supporting empirical data and appeared to be focused on lower-capacity PEAs (class E4 or Type 1) that are not common in regions like Australia, New Zealand, Europe, and Singapore. Thus, this study aims to provide empirical data for the estimation of $F_a$ of higher-capacity PEAs (class E6 or Type 2) represented by AS/NZS 1891.1:2007 PEAs. Thirty-one force–time charts of drop tests conducted on AS/NZS 1891.1-certified PEAs were evaluated, and it was found that the $F_a$ ranged from 3.2 to 4.7 kN with a mean of 3.9 kN. In contrast to the guidance in Z259.16 and Z359.6, the data does not support estimating $F_a$ based on 80% of maximum arrest force. The study also provided empirical basis for approximating $F_a$ using energy-balance calculation. This paper recommends that in the absence of manufacturer’s information on $F_a$ and other test data, a reasonable estimate of $F_a$ of PEA certified to AS/NZS 1891.1:2007 is 3.4 kN (10th percentile) and a conservative estimate is 3.2 kN (minimum). In the absence of publicly available empirical data of PEA certified to other standards, the results in this paper can provide useful guidance for estimation of $F_a$ certified to other similar standards.

Thus far, the typical spray layup process for fabricating sprayed fiber-reinforced polymer (FRP) composites has entirely depended on the know–how and experience of skilled operators in the marine and commercial composite industry. However, there are some limitations on quality control of these retrofit materials by a typical spray layup process, which are as follows: a limited range of fiber loadings in volume and partial defects by the inconsistent thickness of the layer. To overcome these limitations, this study proposes an advanced spray multiple layup process based on a modified gun-calibration technique in conjunction with spray technical skills proposed by the American Composites Manufactures Association. In detail, practical spray skills were adopted to maintain a consistent thickness of the retrofit materials. An advanced gun-calibration technique was suggested to control the various volume fractions of the fibers. Then, following the proposed spray multiple layup process, sprayed FRP laminates were fabricated. Finally, quantitative tests measuring the thickness, volume fraction of the fibers, and mechanical properties of the final laminates were conducted to assess the feasibility of the proposed process as a quality-control method for retrofit materials.

Based on observations of industrial construction sites, it is evident that most industrial projects do not effectively implement historically successful management practices that have the potential to improve construction productivity. Also, these practices are seldom implemented consistently from project to project. To help address this problem, the authors identified essential management practices for productivity improvement and developed the Best Productivity Practices Implementation Index for Industrial Projects (BPPII Industrial) based on these practices. The BPPII Industrial is a method and metric for assessing the planning and implementation levels of key practices needed to ensure high levels of labor productivity. The practices included in the proposed method are those that are widely accepted in academia and industry to have a positive impact on labor productivity. This paper presents the development, verification, and validation of the BPPII Industrial. This paper’s primary contributions are the identification of key productivity practices, quantification of the relative importance of these practices, development of a method to assess the level of implementation of these practices on project productivity performance, and validation of the proposed method based on its implementation on several industrial projects.

The gross domestic product (GDP) of the Canadian construction industry in 2012 amounted to $111 billion, all having been exchanged in the form of invoices. In fact, a typical construction company processes tens of thousands of invoices for payment annually. There are two significant challenges associated with this invoice processing: (1) process costs due to remuneration of the construction owner’s highly paid personnel, and (2) the cost of delayed invoice payments, which is typically a cost absorbed by the contractor that is consequently added to the overall project cost. Ensuring on-time payment of invoices, even when funds are available, can be a challenging exercise because of variety, volume, and the unpredictable number of received invoices. These realities make overdue invoices a pressing problem to be addressed, which in the long term leads to loss in profit and damaged reputation for both contractors and owners. The research presented in this paper utilizes a cohort Markov model to evaluate invoice processing. It seeks to identify and rank bottlenecks to highlight and prioritize opportunities for process improvement, thereby leading to a null-overdue invoice-processing approach. Furthermore, given the stochastic nature of invoice processing, various probabilistic sensitivity analyses are proposed, including an empirical approach that can be used at the experimental design stage where data are either limited or unavailable.

Multicriteria decision-making (MCDM) methods can help designers address the choosing problem in building detailed design. Many, however, appear to assume that all methods are equivalent. This paper argues that differences between MCDM methods matter. The first contribution of this paper is differentiating between the analytical hierarchy process (AHP) and choosing by advantages (CBA) by comparing them through an example. The second contribution is explaining why CBA is superior to AHP for this context. In summary, CBA (1) provides a more context-based analysis than AHP, (2) does not incorporate conflicting judgments for weighing factors as AHP does, (3) does not assume linear trade-offs between factors as AHP does, (4) does not assume that factors have zero as a natural scale as AHP does, (5) focuses on differentiating between alternatives more than AHP, (6) maintains the result of the decision when nondifferentiating factors are removed, whereas AHP may not, and (7) defers subjective judgments until late in the decision-making process, whereas AHP requires expressing them earlier. This presents a significant research finding, considering the wide use of AHP. Further research is needed to assess what range of design decisions CBA supports.

The concession period is one of the critical factors in the arrangement of a build-operate-transfer contract. For infrastructure that has major strategic, economic, and social effects, the concession period should balance the interests among the private investors, the government, and the users. However, existing research has more frequently discussed economic benefits rather than social welfare. In this regard, this paper proposes an improved model based on the traditional concession model by taking into account the case that the government might lower the service price in the posttransfer period to increase social welfare. This paper also introduces the optimal pricing methods for both concession and posttransfer period. The proposed methodology suggests a narrower concession interval, which helps improve the negotiation efficiency between the government and the private investor to a certain extent. The application of the proposed model is demonstrated by a case study of Western Harbor Tunnel in Hong Kong, and the outcome shows that a significant improvement was made when taking into account social welfare.

Construction workers’ attitudes and behaviors are one of the most important factors of a construction project’s performance. As the attention paid to the impact of social influences and norms on worker behavior grows, agent-based modeling and simulation (ABMS) emerges as a research tool for studying workers’ group behavior. With ABMS, researchers can uncover the underlying process of group behavior emerging from individuals’ interactions in an organization. However, validating agent-based simulation with real data is the greatest challenge in using ABMS for organizational behavior research. With this background in mind, the objective of this paper is to propose a methodology for creating an empirically supported agent-based model for studying workers’ behavior influenced by social norms. The proposed methodology suggests that empirical data collected by a questionnaire can be used for ABMS in three steps: (1) testing the agent behavior rules used in an agent-based model (i.e., testing the modeling assumptions), (2) demonstrating the model behavior’s qualitative agreement with real workers’ behavior (i.e., testing the simulation results against real data in a qualitative manner), and (3) creating a specific agent-based model with the model parameters that correspond to a specific empirical case. A specific agent-based model created in this way can then be seen as a scenario generator that corresponds to a specific reality and can be used to answer what if questions. Therefore, the model can be used to develop policies/interventions to improve workers’ behavior in a given situation. The proposed methodology is illustrated by a study on construction workers’ absenteeism that was conducted by the authors. This paper contributes to the body of knowledge of workforce management in construction; the proposed methodology provides a means of simulating workers’ group behavior and developing policies/interventions to improve worker behavior at the group level in construction.

A model has been developed employing an artificial neural network (ANN) to predict the safe work behavior of employees using 10 safety climate constructs determined through literature review. The model utilizes safety climate constructs (determinants) as inputs and safe work behavior as an output. Two hundred twenty-two responses from several construction projects across India were collected through a questionnaire survey. A three-layer feed-forward back-propagation neural network (10-11-1) was appropriate in building this model which has been trained, validated, and tested with sufficient data sets. The model predicts the safe work behavior of employees reasonably well. In addition, a sensitivity analysis was carried out to study the impact of each construct on the safe work behavior of employees. As a result, safety climate constructs like supervisory environment, work pressure, employees’ involvement, personal appreciation of risk, and supportive environment were significantly associated with the safe work behavior of employees. This model has great potential in aiding contractors and clients in promoting safe work behavior and the efficient management of the safety of employees in construction projects.

Lean production is a management philosophy that creates competitive advantages and provides important savings opportunities for companies and organizations. However, there is limited evidence showing to what extent lean production can improve productivity and organizational performance in the mining industry. Mining is a critical industry in Chile, so to understand the impact of lean production, this research provides details regarding the implementation of lean production methodologies in three underground mining development projects belonging to a Chilean mining company. In all of the case studies reviewed, the implementation of lean methodologies generated performance improvements in related projects and construction organizations, with statistically significant improvements in workflow, actual production capacity, operational reliability, productivity, time utilization, and organizational performance. The main findings of this study suggest that the incorporation of lean methodologies has significant potential to improve the performance of mining operations, which is critical given the current and future challenges in the mining sector.

Current procedures for computing building price indices do not adequately account for changes in the quality of new residential construction. This has resulted in an upward bias when it comes to price changes, which causes published productivity changes to be too low because of overdeflation. The aim of this study is to estimate productivity figures that take quality changes into account. Knowledge about the size of quality changes is important to quantify the true level of cost increases in the residential construction sector to obtain more reliable productivity development figures. This paper shows that productivity in the residential construction sector has been underestimated since the 1990s. The findings indicate that data collection for productivity calculations should be more detailed and rigorous to measure quality changes. The paper highlights potential improvements in the calculations of productivity changes.

Space and time planning of construction activities is a key factor affecting safety and field performance in construction sites. When scheduling and controlling linear projects, identification of potential congestion and/or idling can assist in the timely execution of a project. With the advent of location-aware technologies, it becomes possible to track availability of resources in (quasi-)real time during project execution. This provides better oversight over the movement of materials, equipment, and workforce in the jobsite. Space and time floats are currently defined and used separately in the scheduling literature. In this paper, the concept of space-time floats is proposed as a new type of float to simultaneously consider space and time constraints. Space-time floats are envelopes for all possible movement patterns that an activity or its associated resources can take considering the time and space constraints of that activity. Simultaneous consideration of space and time floats makes it possible to trade-off one for the other, which is the significance of this new float. Introduction of the space-time floats into the schedules offers new possibilities to forecast the availability of resources at a specific time and location. Use of the space-time float for each activity also enables the scheduling and control methods to forecast potential zones and times of congestion between activities and detect activity idle times as well as actual and/or potential delays. Eliminating congestions and potential risks for delays have been shown to offer considerable advantages for linear projects. Generation of space-time float for each activity in the project also helps the project team to be informed about the space and time available to them at each time and location, respectively.

Asbestos cement (AC) pipe accounts for approximately 15% of the total length of water distribution system piping in North America. Pipe renewal activities are needed to maintain these pipes as they deteriorate, but concerns over the environmental impact and regulations associated with these construction methods are largely misunderstood. Confusion around these issues led this study to determine (1) what the regulations allow, (2) which renewal methods are applicable, and (3) the critical data needs associated with these methods when used for AC pipe renewal. This paper describes current AC pipe renewal regulations and practices (e.g., pipe bursting, cured-in-place pipe lining, etc.) and the critical data needs relating to AC pipe renewal (e.g., study of the asbestos fibers released in air, water, and soil during construction). These findings are valuable for water utilities in need of solutions for renewing their aging AC pipes and in understanding regulations. The recommendations highlight the needed investigation into the environmental impact of AC pipe renewal construction, which when completed will help clarify future regulations.

This study aims at differentiating construction project critical success factors based on organizational backgrounds of project participants: contractor, construction manager (owner representative), and design firms. Construction professionals from diverse locales were asked to rate a list of 53 potential factors for the objectives of schedule, budget, and quality, and additionally compare the objectives for overall project success. The set of 53 factors was enriched by eliminating 13 factors that could not prove their statistical significance for even one of schedule, budget, or quality performances for any of the three organizations. Commissioning the 40 significant factors, an analytical hierarchy process was used to determine organization-based factor rankings for overall project success. Spearman’s test on rankings of the 40 significant factors for overall project performance results in a highest level of correlation between the managers and contractor personnel ($r_s=0.54$), followed by that between the managers and the designers ($r_s=0.50$), and a least correlation between the contractor personnel and the designers ($r_s=0.19$). Hence for successful project implementation, professional construction managers have an edge over the designers in supervising the contractors.