Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 15 results ...

Arboleda, C A and Abraham, D M (2004) Fatalities in Trenching Operations—Analysis Using Models of Accident Causation. Journal of Construction Engineering and Management, 130(02), 273–80.

Barker, R, Childerhouse, P, Naim, M, Masat, J and Wilson, D (2004) Potential of Total Cycle Time Compression in Construction: Focus on Program Development and Design. Journal of Construction Engineering and Management, 130(02), 177–87.

Chan, A P C, Chan, D W M, Chiang, Y H, Tang, B S, Chan, E H W and Ho, K S K (2004) Exploring Critical Success Factors for Partnering in Construction Projects. Journal of Construction Engineering and Management, 130(02), 188–98.

Choi, H, Cho, H and Seo, J W (2004) Risk Assessment Methodology for Underground Construction Projects. Journal of Construction Engineering and Management, 130(02), 258–72.

Hadikusumo, B H W and Rowlinson, S (2004) Capturing Safety Knowledge Using Design-for-Safety-Process Tool. Journal of Construction Engineering and Management, 130(02), 281–9.

Hegazy, T, Elhakeem, A and Elbeltagi, E (2004) Distributed Scheduling Model for Infrastructure Networks. Journal of Construction Engineering and Management, 130(02), 160–7.

Jeong, H S, Abraham, D M and Lew, J J (2004) Evaluation of an Emerging Market in Subsurface Utility Engineering. Journal of Construction Engineering and Management, 130(02), 225–34.

Lingard, H (2004) Work and Family Sources of Burnout in the Australian Engineering Profession: Comparison of Respondents in Dual- and Single-Earner Couples, Parents, and Nonparents. Journal of Construction Engineering and Management, 130(02), 290–8.

Lu, M and Anson, M (2004) Establish Concrete Placing Rates Using Quality Control Records from Hong Kong Building Construction Projects. Journal of Construction Engineering and Management, 130(02), 216–24.

Sacks, R, Eastman, C M and Lee, G (2004) Process Model Perspectives on Management and Engineering Procedures in the Precast/Prestressed Concrete Industry. Journal of Construction Engineering and Management, 130(02), 206–15.

Shohet, I M and Perelstein, E (2004) Decision Support Model for the Allocation of Resources in Rehabilitation Projects. Journal of Construction Engineering and Management, 130(02), 249–57.

Stewart, J, Minchin, R E, Jaselskis, E J, Dayal, V and Smith, G (2004) Potential Construction Applications for Thermoset Composite Scrap Material. Journal of Construction Engineering and Management, 130(02), 199–205.

Zhang, X (2004) Concessionaire Selection: Methods and Criteria. Journal of Construction Engineering and Management, 130(02), 235–44.

Zheng, D X M, Ng, S T and Kumaraswamy, M M (2004) Applying a Genetic Algorithm-Based Multiobjective Approach for Time-Cost Optimization. Journal of Construction Engineering and Management, 130(02), 168–76.

  • Type: Journal Article
  • Keywords: Optimization; Cost control; Time factors; Algorithms; Project management; genetic algorithms; optimisation; cost optimal control; project management; civil engineering; construction;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2004)130:2(168)
  • Abstract:
    Reducing both project cost and time (duration) is critical in a competitive environment. However, a trade-off between project time and cost is required. This in turn requires contracting organizations to carefully evaluate various approaches to attaining an optimal time-cost equilibrium. Although several analytical models have been developed for time-cost optimization (TCO), they mainly focus on projects where the contract duration is fixed. The optimization objective in those cases is therefore restricted to identifying the minimum total cost only. With the increasing popularity of alternative project delivery systems, clients and contractors are targeting the increased benefits and opportunities of seeking an earlier project completion. The multiobjective model for TCO proposed in this paper is powered by techniques using genetic algorithms (GAs). The proposed model integrates the adaptive weights derived from previous generations, and induces a search pressure toward an ideal point. The concept of the GA-based multiobjective TCO model is illustrated through a simple manual simulation, and the results indicate that the model could assist decision-makers in concurrently arriving at an optimal project duration and total cost.

Zwick, D C and Miller, K R (2004) Project Buyout. Journal of Construction Engineering and Management, 130(02), 245–8.