Dong Zhai

Relationship between Automation and Integration of Construction Information Systems and Labor Productivity

Information technology (IT) has been used to increase automation and integration of information systems on construction projects for over two decades. However, evidence that overall costs have been reduced or project performance has been improved with IT in construction is limited and mostly focused on application specific studies. A comprehensive understanding of the relationship between IT and project performance helps industry practitioners better understand the likely outcomes of implementation of IT application and likewise benefits researchers in improving the effectiveness in their IT development efforts. An opportunity to examine new evidence exists with the emergence of the Construction Industry Institutes Benchmarking and Metrics database on construction productivity and practices. This article presents an analysis of that data to determine if there is a relationship between labor productivity and level of IT implementation and integration. Data from industrial construction projects are used to measure the relationships between the automation and integration of construction information systems with productivity. Using the independent sample t-test, the relationship was examined between jobsite productivity across four trades (concrete, structural steel, electrical, and piping) and the automation and integration of various work functions on the sampled projects. The results showed that construction labor productivity was positively related to the use of automation and integration on the sampled projects.

Model to Predict the Impact of a Technology on Construction Productivity

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 impact of management practices on mechanical construction productivity

Over recent decades, sporadic advancements in machinery and construction materials have to some extent increased construction productivity in the United States. However, there is evidence that additional productivity improvement opportunities exist. One way to improve direct work rates and likewise the potential to increase construction craft productivity is through better planning and management. Utilizing a dataset from the Construction Industry Institute Benchmarking and Metrics programme with 41 sampled projects, the relationship between the level of implementation of different management programmes and mechanical craft productivity is examined. The implementation of several management programmes, including pre‐project planning, team building, automation and integration of information systems and safety had a positive correlation with improved mechanical productivity. In fact, the statistical results show that projects with advanced implementation of the selected management programmes experienced significant mechanical productivity advantages over projects with weak implementation.

Statistical Analysis of the Effectiveness of Management Programs in Improving Construction Labor Productivity on Large Industrial Projects

AbstractThe purpose of this research effort is to identify the effectiveness of a particular set of important management programs in improving construction labor productivity. These programs were previously defined through industry experts and research consensus as having significant impact on project cost, schedule, safety, scope, and quality performance, but their relationship to labor productivity was not known. Through statistical analyses of the database maintained by the Construction Industry Institute’s Benchmarking and Metrics Committee, headquartered in Austin, Texas, United States, the research presented in this article examined whether projects with high levels of implementation of programs related to front-end planning, materials management, automation and integration of information systems, team building, constructability, and safety, experienced better labor productivity among the mechanical, electrical, concrete, and structural steel trades compared to projects with low levels of implementa...

Accuracy analysis of selected tools for estimating contract time on highway construction projects

In many highway construction projects, the time available to complete construction is set by the contract documents. Most transportation agencies have developed tools to assist project planners in estimating the amount of time required to complete the project, but how accurate are these systems in estimating project duration? The current work examines the accuracy of integrated scheduling systems from two state transportation agencies in estimating contract time for previously completed Kentucky highway construction projects. The accuracy of the systems where tested against project data from 66 completed Kentucky Transportation Cabinet Projects. The analysis revealed that the average accuracy of both systems in predicting project duration was greater than ±200% across a variety of projects. A pilot study of the use of multivariate regression analysis of the data from 66 completed Kentucky Transportation Cabinet projects was performed to identify specific combinations of work item quantities with the engineers cost estimate that produce the most accurate estimate of project duration. The accuracy of the developed regression equation in predicting highway construction project duration was ±25%. The current work contributes improved understanding of the accuracy of integrate scheduling systems in predicting highway construction duration.

Using Parametric Modeling to Estimate Highway Construction Contract Time

Federal regulations require that state transportation agencies have written procedures for setting the construction contract time for highway projects. Since the institution of those regulations, state agencies have used a variety of methods to estimate and set contract time. FHWA recommends the use of methods based on production rate and activity precedence and logic in this process. For years, the Kentucky Transportation Cabinet used a system based around these requirements and recommendations only to find that the system produced estimates varying widely from actual construction times. The use of parametric modeling and historic contract times produced a system that more accurately estimates contract times and with an associated Microsoft Excel–based tool is more user friendly. This approach used project cost and bid item quantity data in multivariate regression–based modeling to develop a set of equations that provide the Kentucky Transportation Cabinet with contract time estimates for projects in a matter of minutes by using data readily available at later design stages.