Journal of Construction Engineering and Management

Nonroad construction vehicles and equipment powered by diesel engines contribute to mobile source air pollution. The engines of this equipment emit significant amounts of carbon monoxide, hydrocarbons, nitrogen oxides, and particulate matter. These pollutants pose serious problems for human health and the environment. Therefore, it is necessary to regulate and control the levels of these pollutants. Furthermore, there are emerging requirements and incentives for “greening” of construction vehicle fleets and operations. Currently, there are two types of standards that regulate air pollution for these types of vehicles: technological standards for engines and quality standards for air. It is also necessary to quantify the levels of emissions that nonroad construction vehicles and equipment produce. Quantification may be based on existing data sources (such as the EPA NONROAD model) or by collecting data directly from the vehicles as they work in the field. The purpose of this paper is to introduce the challenges to quantification of emissions from nonroad construction vehicles, describe associated governmental regulations and incentives for reducing emissions, identify and compare various sources of emissions data, establish the need to collect additional data, and propose a future research agenda that focuses on air pollution generated by construction vehicles.

This paper presents a cost estimation model for long-term pavement warranties with multiple distress indicators. One application area for such warranties involves performance-based specifications (PBSs). In contrast to traditional approaches, PBS gives contractors the flexibility to select construction methods, materials, and even design. However, the contractors then must warrant the performance of their work for a specified period of time. Therefore, an accurate estimation of the risks associated with the warranty is a significant cost issue for any contractor to cover potential risks while still being competitive in bidding. Quantitative evaluation of the cost of risk incurred by the warranty has several difficulties. The deterioration of a highway project is a complex process, which is affected by pavement structure, material, traffic load, and weather conditions. Based on a probabilistic risk analysis of failures of performance indicators, the resulting model can estimate the warranty cost at a detailed level. The application of the model has been demonstrated via a numerical case study using long-term pavement performance data.

Accurate prediction of construction costs in the market is essential to effectively estimate costs for construction projects. In the construction industry, cost indexes that are reported in series are often used to explain the change of construction costs. By tracking the trend of such quantitative contemporaneous cost index and making frequent and regular forecasts of the future values of the index, one can develop a deeper understanding of prices of resources used for construction. Incorporating such an understanding and prediction into estimating will help practitioners manage construction costs. This paper proposes two dynamic regression models for the prediction of construction cost index. Comparison of the proposed models with the existing methods proves that the new models provide several advantages and improvements.

This paper describes how float can be calculated exactly for linear schedules by using singularity functions. These functions originate in structural engineering and are newly applied to scheduling. They capture the behavior of an activity or buffer and the range over which it applies and are extensible to an infinite number of change terms. This paper builds upon the critical path analysis for linear schedules, which takes differences between singularity functions and differentiates them. It makes several important case distinctions that extend the earlier concept of rate float. Time and location buffers act along different axis directions. Together with different productivities between and within activities, this can create a complex pattern of critical and noncritical segments. Depending on starts and finishes, areas of float precede or follow these noncritical segments. The schedule of a small project is reanalyzed with case distinctions to demonstrate in detail what float types are generated.

Tracking and monitoring the location of materials on a construction job site is an important, yet commonly overlooked aspect of field data acquisition because timely information about the status of materials, equipment, tools, and labor resources are directly related to the successful completion of a project. With the advanced technologies and innovations in the construction industry, it has become technically viable to implement automated tracking for construction materials. Through the development of an embedded sensor system, this paper illustrates the implementation of pilot experiments examining the accuracy of a system’s performance. A cost-benefit analysis is conducted to illustrate labor savings associated with construction materials handling by comparison between manual and sensor-based materials tracking. The presented embedded sensor system can be extended into diverse application areas in tracking and monitoring framework by providing improved method of field data acquisition and information management.

This study explores the training needs of American supervisors and Hispanic construction craft workers and describes the development, delivery, and evaluation of construction-focused training courses intended to facilitate accommodation for American supervisors and assimilation of Hispanic craft workers to the United States (U.S.) industry. A survey was conducted to better understand the adaptation issues between American supervisors and their Hispanic craft workers. As a result of the survey, two unique construction language courses were developed: English as a second language (ESL) and Spanish as a second language (SSL) for construction. Evaluation feedback from initial course offerings suggest that they were useful in terms of increased confidence and improved ability to communicate. It is anticipated that the courses developed and the training provided will improve the communication channels between American supervisors and Hispanic workers, as well as strengthen the supervisor-worker relationship as the American supervisor grows aware of the existent communication differences. A model for transferring these research findings to other cultural groups and a technology-based approach for delivering these courses to the construction site are also discussed.

At any moment in time, a multitude of factors simultaneously impact construction productivity. Utilizing the knowledge of thousands of construction craft workers, the writers quantitatively analyzed the underlying structure of the factors affecting construction productivity and identified which factors the craft workers consider to be more relatively important as well. This research identified 83 factors affecting construction labor productivity through 18 focus groups with craft workers and their immediate supervisors on nine jobsites throughout the U.S. Next, a nationwide survey was administered to 1,996 craft workers to assess the impact of these factors on construction labor productivity. Principal factor analyses identified 10 latent factors to represent the underlying structure of 83 productivity factors. In addition, the relative importance of the factors’ impact on construction productivity was examined based on the crafts’ union status, trade, and position (craft worker versus foreman). The writers also compared their results to similar previous efforts, and more importantly, identified significant differences that may impact future productivity improvement strategies. This research will help industry and the research community better understand the factors affecting construction labor productivity and more effectively direct future efforts to improve its performance.

With regards to safety, the challenge for researchers and practitioners is to develop work systems that can achieve at the same time high levels of productivity and safety. Towards this objective, this research investigates the work practices of high-reliability crews—that is, crews who perform high-risk work with exceptionally high productivity and safety. The objective of the research is to increase understanding of the work practices that reduce the likelihood of accidents while at the same time increase productivity. This paper presents the findings of an initial exploratory study that compares the work practices of two residential framing crews: one crew with consistently exceptional levels of safety and productivity, and one average-performing crew from the same company. The two crews did not show any difference in safety practices—both crews had high compliance with the company’s safety requirements. However, the production practices of the high-reliability crew were shaped by a clear “guiding principle,” which is a strong focus on avoiding errors and rework, and by specific strategies that support this principle. Such strategies include actions to prevent disruptions, controlling the production pressures, matching skills with task demands, and carefully preparing and coordinating the high demand tasks. These practices prevent production errors while also reducing the likelihood of accidents. The findings provide directions for further research.

The creativity phase is critical to the success of a value engineering exercise, in which the brainstorming technique is deployed to generate ideas. One shortcoming of the brainstorming technique is its lack of direction in problem solving, and consequently the efficiency is low in generating innovative and useful ideas. To overcome this shortcoming, this paper has explored the possibility of incorporating the theory of inventive problem solving (TRIZ) into the workshop session of the value engineering exercise by initiating three new procedures in this session: (1) an initial design procedure to examine the functions of a proposed project; (2) a function trimming procedure to fully utilize existing resources and ensure low life-cycle cost and sustainability of the proposed project; and (3) an interaction analysis procedure to assess the proposed project in a broad perspective with social, economic, and environmental awareness. A case study has indicated the workability of the modified workshop procedures and the usefulness of TRIZ tools and techniques in efficiently and effectively creating innovative ideas.

The principle of learning curves can be applied in construction for the prediction of the time/cycle of future work, work performance levels, and other performance measures. Learning curve principles can be effectively utilized in litigation cases where production is compromised by delays. The objective of this study was to determine if learning curves could be used to accurately predict the production efforts of future units by applying the principles to the prefabrication and driving of prestressed concrete piles. The individual time to cast each of the concrete piles was recorded and used to compute the cumulative average time $\left({\mathrm{CAT}}_N\right)$ to fabricate the concrete piles. The data were used to compute the learning rate $\left(\varphi \right)$ and the theoretical time to complete the first unit $\left(K_c\right)$ . From this information, predictions were made as to the amount of effort to fabricate future piles. The results showed that the pile fabrication crew improved its learning throughout the pile fabrication effort, but this improvement was quite small. The learning curve theory was found to apply well to large numbers of repeated items, and that the predictions made with learning curves are reasonably accurate.

Microtunneling projects have special process characteristics. The production time of microtunneling projects is needed for project estimating and scheduling. The production time has three basic components: (1) preparation times for pipe segments; (2) preparation times; and (3) delay times. Real project data were collected from 35 projects to predict the time needed for pipe segment preparation. A probabilistic distribution technique was used to predict the preparation times for pipe segments. Various numbers of probabilistic distributions were tested against the data. A log-logistic distribution was selected to represent the preparation time’s probability for a pipe in microtunneling projects. Preparation times for high, medium, and low performance contractors were predicted. By enhancing the estimation process of the microtunneling projects, contractors can use this paper in predicting the preparation time for pipe segments, which represents the crew (or crew’s) productivity.