Abstracts – Browse Results
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Abdelhamid, T S and Everett, J G (2002) Physiological Demands during Construction Work. Journal of Construction Engineering and Management, 128(05), 427–37.
Bernold, L E (2002) Spatial Integration in Construction. Journal of Construction Engineering and Management, 128(05), 400–8.
Cheung, S, Suen, H C H and Lam, T (2002) Fundamentals of Alternative Dispute Resolution Processes in Construction. Journal of Construction Engineering and Management, 128(05), 409–17.
Jonasson, S, Dunston, P S, Ahmed, K and Hamilton, J (2002) Factors in Productivity and Unit Cost for Advanced Machine Guidance. Journal of Construction Engineering and Management, 128(05), 367–74.
Karumanasseri, G and AbouRizk, S (2002) Decision Support System for Scheduling Steel Fabrication Projects. Journal of Construction Engineering and Management, 128(05), 392–9.
Kululanga, G K, Price, A D F and McCaffer, R (2002) Empirical Investigation of Construction Contractors' Organizational Learning. Journal of Construction Engineering and Management, 128(05), 385–91.
Ling, Y Y (2002) Model for Predicting Performance of Architects and Engineers. Journal of Construction Engineering and Management, 128(05), 446–55.
- Type: Journal Article
- Keywords: Performance evaluation; Design/build; Architect/engineers; Regression models; performance evaluation; civil engineering; construction industry; personnel; design engineering;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)0733-9364(2002)128:5(446)
- Abstract:
Certain attributes of an architect or engineer (AE) may be used to predict his performance. These attributes may be categorized as “hard” or “soft” attributes. Hard attributes include an AE’s cognitive ability, job knowledge, task proficiency, and job experience. Soft attributes include an AE’s conscientiousness, initiative, social skills, controllability, and commitment. The purpose of this study is to identify those attributes that affect an AE’s performance, and to construct a model to predict his performance in design build (DB) projects. Twenty five attributes were generated using the hierarchy tree. The importance of these attributes was tested with designer/ builders who select and hire AEs, using a standard questionnaire. A statistical test showed that 24 of these attributes are significantly important. Thirty AEs were evaluated by experienced designer/builders (experts) who have worked with them in completed DB projects. Besides giving a global performance score of the AE (dependent variable), each expert also evaluated the AE on the degree to which they exhibited the important attributes (independent variables). Based on these ratings, an optimum multiple regression performance prediction model was obtained. To validate the model, another group of experts used the optimum model to evaluate 18 other AEs. The resulting performance score as calculated by the model was compared to the global performance scores awarded by the designer/builders. This validation process showed the model to be robust. The results of the study reveal that an AE’s performance can be predicted by using just three attributes: AE’s problem solving ability and project approach, AE’s speed in producing design drawings, and the AE’s level of enthusiasm in tackling a difficult assignment.
Lu, M (2002) Enhancing Project Evaluation and Review Technique Simulation through Artificial Neural Network-based Input Modeling. Journal of Construction Engineering and Management, 128(05), 438–45.
Mawdesley, M J, Al-jibouri, S H and Yang, H (2002) Genetic Algorithms for Construction Site Layout in Project Planning. Journal of Construction Engineering and Management, 128(05), 418–26.
Mohamed, S (2002) Safety Climate in Construction Site Environments. Journal of Construction Engineering and Management, 128(05), 375–84.
Skitmore, M and Ng, S T (2002) Analytical and Approximate Variance of Total Project Cost. Journal of Construction Engineering and Management, 128(05), 456–60.