Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 11 results ...

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.

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.

  • Type: Journal Article
  • Keywords: Simulation; Neural networks; Project management; Statistics; Sampling; Artificial intelligence; neural nets; simulation; project management; artificial intelligence; sampling methods; civil engineering;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2002)128:5(438)
  • Abstract:
    Although a stochastic simulation study can eliminate the merge event bias in the project evaluation and review technique (PERT), the errors due to calculating the statistical descriptors of beta distributions with the three-point time estimates of PERT may still make the simulation results suspect. In order to enhance PERT simulation in terms of input modeling, this paper presents an artificial neural network (ANN)-based approach to estimate the true properties of the beta distributions from statistical sampling of actual data combined with subjective information. The minimum and maximum values along with the lower and upper quartiles are four time estimates used to uniquely define a beta distribution. The effects of shape parameters of beta distributions are closely examined, and the working range of shape parameters is defined. To construct an ANN model, data are prepared using random sampling techniques and Excel functions. Through exploring the training data provided, the ANN model has found the patterns between the inputs and the outputs, namely, the interactions and nonlinear relationships among the lower and upper quartiles and the shape parameters of the beta distributions. The ANN model was tested, validated, and compared with other packages for fitting beta distributions such as BetaFit, VIBES, and BestFit. The developed ANN-based input modeling method attempts to embed artificial intelligence into simulation and finds a new way to fit statistical distributions for activity duration in construction simulation, as demonstrated in a sample application.

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.