Journal of Construction Engineering and Management

Much of the project scheduling literature treats task durations as deterministic. In reality, however, task durations are subject to considerable uncertainty, and that uncertainty can be influenced by the resources assigned. The purpose of this paper is to provide the means for contractors to optimally allocate their skilled workers among individual tasks for a single project. Instead of the traditional use of schedules, we develop control policies in the form of planned resource allocation to tasks that capture the uncertainty associated with task durations and the impact of resource allocation on those durations. We develop a solution procedure for the model and illustrate the ideas in an example. The data for the example is collected from a real project.

There are temporal relationships other than the precedence ones between construction activities. Many of these relationships are related to the perspective of intermediate function requirement. The construction industry still lacks a representation schema for capturing the temporal logics residing in this kind of construction requirement. This study presents a schema for representing these temporal logics from three perspectives, namely the construction life cycle of a single product component, the functional interdependencies between two in-progress components, and the availability conditions of an intermediate functionality. In this way, the concurrency relationships among the construction processes can be identified and evaluated, and the intermediate function analysis can be conducted more efficiently, thus contributing to improvement of the executability of construction schedules. Additionally, the temporal logics have been formulated in the format based on temporal interval algebra, which provides a richer semantic representation compared to the traditional precedence relationship types. Finally, using a four-dimensional simulation prototype, a case study of constructing the deck of a bridge by the balanced cantilever approach is modeled to demonstrate the representation of the three perspectives of intermediate function requirements.

In a typical construction project, a contractor may often find that the time originally allotted to perform the work has been severely reduced. The reduction of time available to complete a project is commonly known throughout the construction industry as schedule compression. Schedule compression negatively impacts labor productivity and consequently becomes a source of dispute between owners and contractors. This paper examines how schedule compression affects construction labor productivity and provides a model quantifying the impact of schedule compression on labor productivity based on data collected from 66 mechanical and 37 sheet metal projects across the United States. The model can be used in a proactive manner to reduce productivity losses by managing the factors affecting productivity under the situation of schedule compression. Another useful application of the model is its use as a litigation avoidance tool after the completion of a project.

Craftsmen performing construction work are in the best position to determine how a job should be performed safely. They should be involved in planning the safe execution of the work. Involvement will be a function of opportunity, capability, and motivation. A survey of contractor management personnel and craftsmen was conducted to assess their perceptions of these variables. A simulated planning exercise was conducted to assess craftsmen’s capabilities, in terms of knowledge, of construction tasks and risks. Craftsmen were found to be capable, but inexperienced in reducing their knowledge to writing. Questioning uncovered the depth of their knowledge. Craftsmen perceive themselves as capable and motivated and seek the opportunity to be involved in planning for health and safety.

This study investigates critical factors that significantly affect the benefits of an innovation to the clients and the project as a whole during implementation from the perspective of construction clients. A survey was conducted in Hong Kong, and data from 35 projects that had adopted innovations were collected via mailed questionnaires from project clients. The questionnaire requested respondents to provide specific data about their projects, the type of innovations adopted, and the enablers and barriers to innovation. We found that innovative solutions were particularly beneficial if they were mainly intended to solve technical difficulties in projects and if they were implemented by forming separate implementation teams and by establishing coordination and monitoring mechanisms at the project level. Moreover, we found that the construction clients’ technical capabilities, provision of special training opportunities, and management interests before and after the innovation was implemented were also factors in attaining innovation benefits.

The purpose of construction management is to successfully accomplish projects, which requires a continuous monitoring and control procedure. To dynamically predict project success, this research proposes an evolutionary project success prediction model (EPSPM). The model is developed based on a hybrid approach that fuses genetic algorithms (GAs), fuzzy logic (FL), and neural networks (NNs). In EPSPM, GAs are primarily used for optimization, FL for approximate reasoning, and NNs for input-output mapping. Furthermore, the model integrates the process of continuous assessment of project performance to dynamically select factors that influence project success. The validation results show that the proposed EPSPM, driven by a hybrid artificial intelligence technique, could be used as an intelligent decision support system, for project managers, to control projects in a real time base.

Range estimating is a simple form of simulating a project estimate by breaking the project into work packages and approximating the variables in each package using statistical distributions. This paper explores an alternate approach to range estimating that is grounded in fuzzy set theory. The approach addresses two shortcomings of Monte Carlo simulation. The first is related to the analytical difficulty associated with fitting statistical distributions to subjective data, and the second relates to the required number of simulation runs to establish a meaningful estimate of a given parameter at the end of the simulation. For applications in cost estimating, the paper demonstrates that comparable results to Monte Carlo simulation can be achieved using the fuzzy set theory approach. It presents a methodology for extracting fuzzy numbers from experts and processing the information in fuzzy range estimating analysis. It is of relevance to industry and practitioners as it provides an approach to range estimating that more closely resembles the way in which experts express themselves, making it practically easy to apply an approach.

Due to increased government regulations on environment, health, and safety, the cost of on-site bridge painting has quadrupled over the past several years. The construction industry faces a great challenge in how to control the increased costs of bridge painting and meet the regulations at the same time. A possible solution to address this challenge is to develop a robotic bridge painting system. The development of the robotic system can be justified by the potential improvements in safety and productivity. This paper presents the development and testing of an Intelligent Painting Process Planner. The Planner, built based on bridge feature scheme, is the key component for the robotic bridge painting system that integrates the painting process planning, robot path planning, cost optimization, and quality control functions. During the development process, lab experiments were conducted to determine the values of painting process planning parameters and coating thickness distribution functions. Field tests demonstrated that the prototype robotic bridge painting system achieved the specified painting quality using the parameter values provided by the Planner. Areas that need to be improved in the future were also identified.