Abstracts – Browse Results
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Bobick, T G (2004) Falls through Roof and Floor Openings and Surfaces, Including Skylights: 1992–2000. Journal of Construction Engineering and Management, 130(06), 895–907.
Chan, S and Leung, N (2004) Prototype Web-Based Construction Project Management System. Journal of Construction Engineering and Management, 130(06), 935–43.
Cheah, C Y J, Garvin, M J and Miller, J B (2004) Empirical Study of Strategic Performance of Global Construction Firms. Journal of Construction Engineering and Management, 130(06), 808–17.
Cheng, E W L and Li, H (2004) Development of a Practical Model of Partnering for Construction Projects. Journal of Construction Engineering and Management, 130(06), 790–8.
Cheung, S O, Yiu, K T W and Suen, H (2004) Construction Negotiation Online. Journal of Construction Engineering and Management, 130(06), 844–52.
Chung, T H, Abraham, D M and Gokhale, S B (2004) Decision Support System for Microtunneling Applications. Journal of Construction Engineering and Management, 130(06), 835–43.
Fang, D, Fong, P S and Li, M (2004) Risk Assessment Model of Tendering for Chinese Building Projects. Journal of Construction Engineering and Management, 130(06), 862–8.
Fang, D, Li, M, Fong, P S and Shen, L (2004) Risks in Chinese Construction Market—Contractors’ Perspective. Journal of Construction Engineering and Management, 130(06), 853–61.
Hanna, A S, Camlic, R, Peterson, P A and Lee, M (2004) Cumulative Effect of Project Changes for Electrical and Mechanical Construction. Journal of Construction Engineering and Management, 130(06), 762–71.
- Type: Journal Article
- Keywords: Construction management; Project management; Electrical equipment; Mechanical system, structural;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)0733-9364(2004)130:6(762)
- Abstract:
Change is inevitable on construction projects, primarily because of the uniqueness of each project and the limited resources of time and money that can be spent on planning, executing, and delivering the project. Change clauses, which authorize the owner to alter work performed by the contractor, are included in most construction contracts and provide a mechanism for equitable adjustment to the contract price and duration. Even so, owners and contractors do not always agree on the adjusted contract price or the time it will take to incorporate the change. What is needed is a method to quantify the impact that the adjustments required by the change will have on the changed and unchanged work. Owners and our legal system recognize that contractors have a right to an adjustment in contract price for owner changes, including the cost associated with materials, labor, lost profit, and increased overhead due to changes. However, the actions of a contractor can impact a project just as easily as those of an owner. A more complex issue is that of determining the cumulative impact that single or multiple change orders may have over the life of a project. This paper presents a method to quantify the cumulative impact on labor productivity for mechanical and electrical construction resulting from changes in the project. Statistical hypothesis testing and correlation analysis were made to identify factors that affect productivity loss resulting from change orders. A multiple regression model was developed to estimate the cumulative impact of change orders. The model includes six significant factors, namely: Percent change, change order processing time, overmanning, percentage of time the project manager spent on the project, percentage of the changes initiated by the owner, and whether the contractor tracks productivity or not. Sensitivity analysis was performed on the model to study the impact of one factor on the productivity loss (%delta). The model can be used proactively to determine the impacts that management decisions will have on the overall project productivity. They may also be used at the conclusion of the project as a dispute resolution tool. It should be noted that every project is unique, so these tools need to be applied with caution.
Koksal, A and Arditi, D (2004) Predicting Construction Company Decline. Journal of Construction Engineering and Management, 130(06), 799–807.
Lee, H, Yu, J and Kim, S (2004) Impact of Labor Factors on Workflow. Journal of Construction Engineering and Management, 130(06), 918–23.
Marzouk, M and Moselhi, O (2004) Fuzzy Clustering Model for Estimating Haulers’ Travel Time. Journal of Construction Engineering and Management, 130(06), 878–86.
Rowe, G M, Meegoda, J N, Jumikis, A, Sharrock, M J, Bandara, N and Hettiarachchi, C H (2004) NJTxtr—A Computer Program Based on LASER to Monitor Asphalt Segregation. Journal of Construction Engineering and Management, 130(06), 924–34.
Senouci, A B and Eldin, N N (2004) Use of Genetic Algorithms in Resource Scheduling of Construction Projects. Journal of Construction Engineering and Management, 130(06), 869–77.
Son, J and Mattila, K G (2004) Binary Resource Leveling Model: Activity Splitting Allowed. Journal of Construction Engineering and Management, 130(06), 887–94.
Tam, C M, Tong, T K L and Wong, Y W (2004) Selection of Concrete Pump Using the Superiority and Inferiority Ranking Method. Journal of Construction Engineering and Management, 130(06), 827–34.
Thomas, H R, Horman, M J and de Souza, U E L (2004) Symbiotic Crew Relationships and Labor Flow. Journal of Construction Engineering and Management, 130(06), 908–17.
Tsao, C C Y, Tommelein, I D, Swanlund, E S and Howell, G A (2004) Work Structuring to Achieve Integrated Product–Process Design. Journal of Construction Engineering and Management, 130(06), 780–9.
Walsh, K D, Hershauer, J C, Tommelein, I D and Walsh, T A (2004) Strategic Positioning of Inventory to Match Demand in a Capital Projects Supply Chain. Journal of Construction Engineering and Management, 130(06), 818–26.
Whalen, T M, Gopal, S and Abraham, D M (2004) Cost-Benefit Model for the Construction of Tornado Shelters. Journal of Construction Engineering and Management, 130(06), 772–9.