Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 6 results ...

Olatunji, S O, Olawumi, T O and Aje, I O (2017) Rethinking Partnering among Quantity-Surveying Firms in Nigeria. Journal of Construction Engineering and Management, 143(11).

Rudeli, N, Santilli, A, Puente, I and Viles, E (2017) Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database. Journal of Construction Engineering and Management, 143(11).

Tavakolan, M and Etemadinia, H (2017) Fuzzy Weighted Interpretive Structural Modeling: Improved Method for Identification of Risk Interactions in Construction Projects. Journal of Construction Engineering and Management, 143(11).

  • Type: Journal Article
  • Keywords: Risk interactions; Interpretive structural modeling (ISM) method; Fuzzy logic; Construction projects; Matrice d’Impacts Croises-Multiplication Appliqúe an Classment (MICMAC); Project planning and design;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001395
  • Abstract:
    The construction industry has a major role in the economic development of any country. Because of the nature of the construction industry and its unique characteristics, and the overtime work and overbudget funds spent on these projects, there are many risks which may occur during a project’s lifecycle. Should a specific risk happen, the probability and impact of the other risks will be affected, which often affects the project’s objectives. Therefore it is necessary to consider risk interaction in order to improve the management of project risks. This paper proposes the fuzzy weighted interpretive structural modeling (FWISM). A two-round Delphi method is applied by asking 10 experts to specify the importance of each risk interaction with a fuzzy number due to the uncertain nature of the risks. This paper presents a network of risk interactions in construction projects which then provide the necessary means for exploring the influence of and dependence among the risk factors. In order to identify the key factors that drive the system, MICMAC analysis is applied. The results show that contractual anomalies most influence the other risks of a given project, whereas certain risks such as construction delay or interruptions are more susceptible to these influences than are other risks.

Thabet, W and Lucas, J (2017) Asset Data Handover for a Large Educational Institution: Case-Study Approach. Journal of Construction Engineering and Management, 143(11).

van den Berg, M, Voordijk, H, Adriaanse, A and Hartmann, T (2017) Experiencing Supply Chain Optimizations: A Serious Gaming Approach. Journal of Construction Engineering and Management, 143(11).

Wanberg, J, Javernick-Will, A and Taylor, J E (2017) Mechanisms to Initiate Knowledge-Sharing Connections in Communities of Practice. Journal of Construction Engineering and Management, 143(11).