Journal of Construction Engineering and Management

This paper describes a methodology developed to support decision makers in planning work zones for pavement maintenance and rehabilitation interventions, integrating cost, duration, and user effects. The methodology relies on two key stages. First, a computer model generates the set of feasible options for work zone layouts and schedules, and combines them to include all options for the intervention’s planning and undertaking. Subsequently, a multiple-criteria decision model is used to compare all the alternatives included in the previously obtained set using agency cost, duration, and traffic delay as criteria, according to the weightings that correspond to the decision-maker’s preferences. It constitutes an advance in the current knowledge by avoiding the prior characterization of each alternative and, based on the novel integration of the mentioned criteria, pointing towards the set of alternatives that merit additional analysis. Two case studies with different traffic levels are discussed, showing the capabilities of this methodology while identifying the most adequate alternatives for each context.

Variation is a root cause to waste in a production system because it creates interruptions in the production system. Variation is dissipating through the production flow and reduces productivity; therefore, to minimize the effect, variations need to be handled with great care. This study examined how task starting time and duration is affected by variation. By simulating a sequence of work tasks using a normal distribution, it has been possible to analyze variations’ effect on task starting time, task durations, crew waiting time, and so on. Four situations were simulated, and teen iterations were calculated and analyzed. The analysis revealed that variation itself does not create waste. Waste is only emerging between handoffs; thus, increasing work task duration decreases the effect of variation. Moreover, by comparing a linear sequence and a network of activities, the effect of variation was found to depend on the design of the sequence.

Despite strong advancements in construction safety performance over the past few decades, injuries still occur at an unacceptable rate. Researchers have shown that risk-taking behavior, originating mainly from inaccurate perception and unacceptable tolerance of safety risk, is a significant factor in a majority of construction injuries. Based on psychology research that suggests cognitive interactions between emotions and risk perception, the hypothesis was formed that there are objectively measureable differences in construction safety risk perception between people in different emotional states. To test this hypothesis, a controlled experiment was designed and conducted that (1) induced various positive and negative emotions in 69 subjects using validated movie excerpts; (2) measured emotional states using a validated post-film questionnaire; (3) exposed participants to construction hazards within a high fidelity virtual environment; and (4) measured subjects’ perceptions of the risk related to these hazards adopting a validated frequency-severity-based questionnaire. Once these data were collected, a principal component analysis (PCA) was performed to identify uncorrelated groups of related emotions. A Kruskal-Wallis test followed by multiple Mann-Whitney U tests was then used to test for differences in risk perception between participants belonging to these different emotional groups. The results of these analyses indicated that the mild negative group (sad and unhappy subjects) and the intense negative group (fearful, anxious, and disgusted subjects) perceived statistically significantly ($p=0.003$) more risk than the positive group (happy, amused, joyful, and interested subjects). The two negative groups were also found to perceive more risk than the neutral group ($p<0.02$). However, no statistically significant difference in risk perception was found between the positive and neutral groups or between the two negative groups. According to situational awareness literature, the implication of these findings is that individuals in positive and neutral emotional states may be more prone to engage in risk-taking behaviors than their counterparts because they perceive less risk in the same environment.

Considering the learning effect while planning and scheduling repetitive construction projects can help provide a realistic forecast of their duration and resource requirements. This paper has developed an improved learning curve (LC) to replace the log-linear LC for monitoring improvement in workers’ performance. The improved LC assumes that the manual work time of the specific unit converges to a nonzero constant rather than zero under a large number of repetitions. It also takes into account the influence of workers’ prior experience and machinery in the learning process. An improved line-of-balance (LOB) model is then presented by integrating the proposed LC and a mechanism of resource allocation. The proposed LOB model can make use of the learning effect to minimize the total resource usage of a project, while meeting the requirements for work continuity and the target deadline of each activity. An illustrative example has been cited to demonstrate the capability of the model. The technique can help planners use the learning effect to get a more realistic and optimal schedule about resource utilization.

A variety of building information modeling (BIM) performance evaluation initiatives have been proposed to quantify BIM utilization capacity of enterprises. These initiatives were designed for evaluating, instead of benchmarking, an organization’s performance in BIM utilization. Unlike evaluation that mainly focuses on ascertaining the achievement of BIM utilization within an organization, benchmarking is more interested in comparing one organization’s BIM performance to their industry peers. By identifying gaps in specific areas, decisions to make improvements can be facilitated. This paper proposes a cloud-based BIM performance benchmarking application called building information modeling cloud score (BIMCS) to automatically collect BIM performance data from a wide range of BIM users nationwide. It utilizes the software as a service (SaaS) model of cloud computing to make the collection, aggregation, and presentation of benchmarking data autonomous and interactive. Based on the big data collected from the BIMCS database, an overall view of the industry status quo of BIM utilization may be obtained, and ultimately, a protocol for BIM performance can be developed on the basis of a better knowledge discovery process. Furthermore, BIMCS data will help individual companies compare and improve their performance in BIM utilization with respect to their industry competitors.

Variability is a major problem in construction projects. It can negatively affect performance and disrupt production. In the past two decades, a considerable amount of research has been undertaken to select a suitable statistical model that can accurately model this variability. Although the beta distribution has achieved extensive acceptance to model process durations in construction, certain production situations can constrain its modeling capabilities. The limitations are apparent when a coefficient of variation associated with the process variability is higher than 100%. The aim of this research is to explore a reliable probability distribution function that can model the situations in which the beta distribution is not suitable. Data from 73 construction samples corresponding to 25 projects from three different countries were analyzed as a case study by using statistical inference techniques and stochastic simulation. The main finding from this research shows that the Burr distribution was a more accurate statistical function for modeling processes with variability levels between 100 and 150% in comparison to other commonly used distributions. The results enable construction modelers to represent and simulate the process variability with an increased accuracy in a wider range of variability. Accordingly, suitable production strategies can be designed to cope with variability in construction.