Journal of Construction Engineering and Management

The problem of accurate estimation of earthmoving productivity has intrigued many researchers for many years, but there is yet no model that will predict the output of such operations with a satisfactory degree of confidence for all situations. The problem is exacerbated by the occurrence of hauler bunching that results in many deterministic models overestimating productivity. Systems analysis methods can be used, such as queuing theory or simulation, but they are generally impractical to the average site planning engineer. This paper investigates the results obtained from over 140 separate earthmoving operations taken from four different highway construction projects. Initially, the effects of bunching are determined, and many factors that influence productivity are identified. To investigate the relationships between earthmoving operating conditions and both productivity and bunching, and to develop a deterministic model that will enable earthmoving operations to be planned for many different situations with relative ease, stepwise multiple regression techniques are used. The results indicate that there is a strong linear relationship between operating conditions and productivity.

The identification of key factors for construction project success enables appropriate allocation of limited resources. Most of the related past work only identified critical success factors for construction projects in general. The present study seeks to distinguish these factors according to the project objectives of budget, schedule, and quality. The analytic hierarchy process is adopted to determine the relative importance of success-related factors. A hierarchical model for construction project success is presented. Sixty-seven success-related factors are considered. These factors are grouped under four main project aspects, namely, project characteristics, contractual arrangements, project participants, and interactive processes in the hierarchical model for project success. A questionnaire was developed to facilitate systematic data collection in this study. Experts with an overall average of 20 years of experience in the construction industry were invited to participate in the survey. Critical success factors addressing budget performance, schedule performance, quality performance, and overall project success are identified. Some pertinent findings of the study are discussed. Comparisons with findings of previous studies using neural network approach are also presented.

A database program called WorkPlan has been created to systematically develop weekly work plans. Such work plans are used by crew foremen in scheduling work packages and allocating available labor and equipment resources. WorkPlan adopts the Last Planner methodology, which implements several lean construction techniques. A week prior to conducting work, WorkPlan guides the user step by step through the process of spelling out work packages, identifying constraints, checking constraint satisfaction, releasing work packages, and allocating resources; then at the end of the week, collecting field progress data and reasons for plan failure. This systematic approach helps the user create quality work plans and learn from understanding reasons for failure. The lean planning philosophy underlying WorkPlan and the functionality of the computer program implementation are detailed in this paper. Various ways of displaying work package data are illustrated. WorkPlan's job-shop scheduling view complements the view traditionally adopted by project management, as is reflected in scheduling tools using the critical-path method.

Partnering is a technique that has become the construction industry's application of total quality management and enjoys widespread use throughout the industry. This paper details the results of a statistical analysis of over 400 Texas Department of Transportation construction projects worth a total investment of nearly $2.1 billion. Half the projects were completed under partnering agreements. After comparing the two groups of projects, the paper concludes that partnering promises to furnish a means to control the two prime project performance indicators—cost growth and time growth. Additionally, in a large construction program the continuous application of partnering will result in improved project performance across the entire program.

Resource allocation and leveling are among the top challenges in project management. Due to the complexity of projects, resource allocation and leveling have been dealt with as two distinct subproblems solved mainly using heuristic procedures that cannot guarantee optimum solutions. In this paper, improvements are proposed to resource allocation and leveling heuristics, and the Genetic Algorithms (GAs) technique is used to search for near-optimum solution, considering both aspects simultaneously. In the improved heuristics, random priorities are introduced into selected tasks and their impact on the schedule is monitored. The GA procedure then searches for an optimum set of tasks' priorities that produces shorter project duration and better-leveled resource profiles. One major advantage of the procedure is its simple applicability within commercial project management software systems to improve their performance. With a widely used system as an example, a macro program is written to automate the GA procedure. A case study is presented and several experiments conducted to demonstrate the multiobjective benefit of the procedure and outline future extensions.

Change orders have become an everyday occurrence in construction. It is widely accepted by both owners and contractors that change orders have an effect on the labor efficiency, but these effects are difficult to quantify and frequently lead to disputes. Data from 61 mechanical construction projects were collected to develop a statistical model that estimates the actual amount of labor efficiency lost due to the change orders. The input variables needed in the model are as follows: (1) The original estimated labor hours; (2) impact classification; (3) total estimated change hours; (4) number of change orders; and (5) the timing of changes. The results of this study show that impacted projects have a larger decrease in labor efficiency than unimpacted projects. Additionally, the later a change order occurs in the life of a project the more impact it will have on the labor efficiency. The results appear to be consistent with the intuition of experienced professionals. Although each project has unique characteristics, the resultant model provides owners and contractors with a baseline measure of lost labor efficiency.

Artificial neural networks (ANNs) have been applied to support construction mark-up estimation. The major drawback of this application, however, is that an ANN system is unable to explain why and how a particular recommendation is made. This significantly affects the user-acceptance of the system and its results. The research presented in this paper investigates the use of the KT-1 method for automatically extracting rules from a trained neural network. The KT-1 method is implemented and tested on collected bidding data, and the results from the investigation indicate the usefulness of the KT-1 method. Discussions on the difficulties of generating automated explanations are also presented.

The reforms introduced by the International Monetary Fund in Asia will bring about greater transparency in the economies and innovative approaches in procurement of contracts. China has taken new initiatives in build, operate, and transfer (BOT) infrastructure projects. It is, however, important to analyze and manage the unique or critical risks associated with China's BOT projects. This is especially so after new policies were introduced in late 1996 when the first state-approved BOT project, the U.S. $650,000,000 2 × 350 MW coal-fired Laibin B Power Plant (Laibin B), was awarded. These include the competitive tendering process and 100% foreign ownership of the operating company. An international survey on risk management of BOT projects in China was conducted to seek the views of practitioners. The overall objectives of the survey are (1) to identify the unique or critical risks associated with China's BOT projects; (2) to evaluate the effectiveness of measures for mitigating these risks; (3) to examine the key contract clauses used in Laibin B's concession agreement; and (4) to provide a risk management framework for investing in future BOT projects in China. This paper discusses the adequacy of key contract clauses in Laibin B's concession agreement related to the political and force majeure risks in China, from the perspective of foreign developers, lawyers, and lenders. The contract clauses discussed include changes in law, corruption, delay in approval, expropriation, and force majeure. Areas for further improvement in the clauses are suggested.

Providing superior quality and keeping customers satisfied are rapidly becoming the ways construction companies differentiate themselves from competitors. Many companies are, however, frustrated in their efforts to improve quality and customer satisfaction through the implementation of total quality management (TQM). The primary objective of this study was to increase understanding of how TQM affects home-buyer satisfaction. For the first time an empirical study has confirmed that implementation of TQM is positively associated with home-buyer satisfaction. Supplier quality management has emerged as the most important factor in shaping home-buyer satisfaction, but it is the area in which home builders do not practice extensively.

Construction clients are increasingly calling for bids that require competing contractors to submit both the bid price and contract time. In such situations contractors are faced with the problem of deciding which combination of bid price and contract time to submit. An important consideration is the cost of time to the client. This varies from contract to contract and is usually expressed in terms of a unit time value such as liquidated damages rate or, for highway contracts, a daily road user cost. The unit time value is normally set out in the contract appendix and often used by the client as a basis for determining the contractor's total combined bid. This kind of procurement method is commonly referred to as the A + B method. To illustrate the mechanism of price-time biparameter procurement, a total combined bid Iso-map is developed in this study. The contractor's price-time performance curve is then incorporated into the map to determine the contractor's optimal bid parameters: tender price and contract time. Also, based on this mechanism, a mathematical optimization bid model is developed for calculating the optimal bid parameters.