Journal of Construction Engineering and Management

Continuous real-time estimating of compaction quality during the construction of a hot mix asphalt (HMA) pavement is addressed in this paper. The densification of asphalt pavements during construction usually is accomplished by using vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. The measured vibrations of the compactor along with process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the asphalt mat density. Contrary to existing techniques in the literature in which a model is developed to fit experimental data and to predict mat density, a neural network-based approach is adopted that is model-free and uses pattern-recognition techniques to estimate density. The neural network is designed to read the entire frequency spectrum of roller vibrations and to classify these vibrations into different levels. The intelligent asphalt compaction analyzer (IACA) is then trained to convert these vibration levels into a “number” indicative of the asphalt mat density at a given location. This two-step process eliminates the need for regression analysis and produces more accurate density measurements than those reported elsewhere in the literature. Compaction studies of HMA mixes on a stiff subgrade indicate that the changes in the vibration characteristics of the roller are attributable to an increased compaction of the HMA base. The results also show that, with the neural network working as a classifier, the IACA can estimate the density continuously, and in real time, with accuracy levels adequate for quality control in the field.

Projects must meet budget, schedule, safety, and quality goals to be regarded as a success. Many factors come into play, and many decisions are made that influence a project’s outcome. Today, owners are often faced with deciding between an execution strategy that emphasizes either project cost or project schedule. Such a decision may be made not once, but throughout the life of the project. Project teams, when required by the owner to make a cost-schedule trade-off, generate common sense ideas, best practices, and other means of achieving the desired trade-off through brainstorming sessions. This paper offers a tool for systematically identifying the techniques which are most effective in achieving the trade-off goal. Project teams can then use these techniques in a timely fashion to increase the likelihood of project success.

Construction often involves considerable time gaps between cost estimation and on-site operations. In addition, many operations are performed over a considerable period of time. Accordingly, estimating construction costs must consider the trend of costs in the market, where construction costs normally change over time. Insight into the trend of construction costs in the market, therefore, is beneficial, even critical, to the effective cost management of construction projects. In an effort to support such insight development, two time series models were built by analyzing time series index data and comparing them with existing methods in the present study. The developed time series models accurately predict construction cost indexes. In particular, the models respond sensitively and swiftly to a quick, large change of costs, which allows for accurate forecasting over the short- and long-term periods. Overall, the models are effective for understanding the trend of construction costs.

In this research, variation is defined as the time difference between what was planned and what actually happened in terms of task starting times and duration. Variation in construction tasks is important as it can impact productivity performance. Construction projects consist of a large number of interdependent tasks. When the starting time and/or duration of one task varies, it can affect other downstream tasks and result in disruptions to the schedule and/or decreased productivity. The construction process is complex and involves numerous people with different levels of responsibility, which makes identifying the root causes of the variation difficult. A nationwide survey was administered to workers, foremen, and project managers to identify the most prevalent causes and magnitude of both starting time and task duration variation. Fifty individual causes of variation were divided into eight categories: prerequisite work, detailed design/working method, labor force, tools and equipment, material and components, work/job site conditions, management/supervision/information flow, and weather or external conditions. This research examined the similarities and differences in perceptions between craft workers, foremen, and project managers in terms of starting time and task duration variation. The top eight causes of starting time variation and top nine causes of task duration variation were identified. The research also quantitatively analyzed the underlying structure of the causes of variation using factor analysis. This was done by grouping the 50 individual causes into nine orthogonal factors that represent the underlying structure of the affecting causes. The findings will help construction project managers and field managers focus on the root causes of variation during planning in order to develop effective strategies to reduce variation and improve project productivity performance.

Although some new technologies promise to improve construction productivity, their ability to deliver is not always realized. Building on a great deal of prior research, a four-stage predictive model was developed and validated to estimate the potential for a technology to have a positive impact on construction productivity. The four stages examine the costs, feasibility, usage history, and technical impact of a technology. The predictive model combines results from historical analyses to formalize how selected technologies with improved construction productivity can be used as a predictor of how future technologies might do the same. Each of the stages of a predictive model was subdivided into a series of categories and questions, which were weighted by importance by using the analytic hierarchy process and historical analysis to generate a performance score for the analyzed technology. The predictive model was then validated by using 74 previous and existing construction technologies. Statistical analysis confirmed that average performance scores produced by the model were significantly different across the categories of successful, inconclusive, and unsuccessful in the actual implementation experience of technologies.

The influx of Hispanic workers helped the U.S. construction industry alleviate its shortage of craft workers in the last decade. In 2009, Hispanics accounted for nearly a quarter (22.5%) of the construction workforce in the United States. However, no research has been conducted to examine how various factors influence Hispanic craft workers’ productivity. This paper analyzes the data from a nationwide survey to obtain craft workers’ perspective on construction productivity. The respondents were categorized as Spanish- or English-speaking workers according to their declared primary language, irrespective of their ethnic background. The findings reveal that Spanish- and English-speaking craft workers generally agreed on the priority of the factors affecting labor productivity. However, Spanish-speaking workers rated factors associated with supervisor direction, safety, and labor more severely than English-speaking craft workers. Meanwhile, English-speaking craft workers perceived factors related to engineering drawing management as being more detrimental to productivity than did Spanish-speaking craft workers. Specifically, in comparison with English-speaking craft workers, Spanish-speaking craft workers experienced more severe issues with communicating with their supervisors, pay and monetary bonus for good performance, and lack of training on safety, health, and skills. These findings should be valuable for project management to effectively improve labor productivity of their Spanish-speaking craft workforce.

Value management (VM) is widely regarded as a useful tool for management to meet the challenges, such as limited resources and tight schedules arising in the construction industry. A rigorous measurement on the performance of VM studies is likely to improve the implementation of the VM methodology and enhance the confidence of clients about their investment in VM. The identification of key performance indicators (KPIs) is an essential first step in developing a proper performance measurement framework. This paper aims to identify the KPIs for measuring the performance of VM studies in construction. Delegates of international VM conferences hosted by SAVE International and Hong Kong Institute of Value Management during the period 2005 to 2007 were used as the target group for a questionnaire survey. The survey results identified 18 KPIs out of 47 potential performance indicators. They are divided into three groups: predicting indicators, process-related indicators, and outcome-related indicators, according to their characteristics. Three principal components were identified by using factor analysis of the KPIs, which reveals the interrelationship among the KPIs. Details on how to implement these KPIs, such as data providers, weightings, and scoring methods, are also presented.

Buffers have been commonly used as a production strategy to protect construction processes from the negative impact of variability. Construction practitioners and researchers have proposed several buffering approaches for different production situations and contexts. However, these solutions have been impractical for managing buffers. To overcome this, this study proposes a new site methodology for managing work-in-process (WIP) buffer in repetitive projects, on the basis of the reliable commitment model (RCM). RCM is a decision-making tool based on lean principles, which uses statistical models to develop more reliable work plans at the operational level. RCM helps to manage WIP buffer in work plans by using site information and planning reliability indicators that result in improved project performance, such as labor productivity and process progress. A repetitive building project was used as a case study. The main finding was that labor productivity, process progress, and waiting times improved when using larger WIP buffers than those typically used among crews. This shows the potential of RCM as a practical tool to manage WIP buffer sizes and to promote the use of lean production strategies at the operational level.