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Site layout planning can affect productivity and is crucial to project success. However, as construction is heterogeneous in the nature of its organizations, project designs, time constraints, environmental effects, etc., site layout planning for each project becomes unique. Affected by many uncertainties (variables) and variations, site layout planning is a typical multiobjective problem. To facilitate the decision-making process for these problems, a nonstructural fuzzy decision support system (NSFDSS) is proposed. NSFDSS integrates both experts' judgment and computer decision modeling, making it suitable for the appraisal of complicated construction problems. The system allows assessments based on pairwise comparisons of alternatives using semantic operators that can provide a reliable assessment result even under the condition of insufficient precise information. DOI: 10.1061/(ASCE)0733-9364(2002)128:3(220) CE Database keywords: Decision support systems; Fuzzy sets; Site preparation, construction.

Accuracy in estimating activity duration is one of the key prerequisites for successful construction planning. Efficient material transportation plays an important role in reducing costs and time. Time measurement and work-study techniques can provide good estimation of activity duration, but forming the databank for various conditions is expensive. The use of empirical models has been developed as an alternative to overcome the deficiency while maintaining a reasonable accuracy. In this research traditional linear regression models and nonlinear neural network models have been developed for predicting hoisting times of a tower crane. It is found that nonlinear neural network models can achieve higher accuracy. However, planners may find that the regression models, which describe the relationship between the variables in more simplistic terms, could allow them to shorten the hoisting times by manipulating the input variables. The results and the merits of the models are discussed. DOI: 10.1061/(ASCE)0887-3801(2002)16:1(76) CE Database keywords: Neural networks; Cranes; Time studies; Construction management; Multiple regression models; Nonlinear analysis.

Accurate assessment of hoisting times is crucial for planning construction activities. The objective of this paper is to develop reasonably accurate prediction models to assess hoisting times of tower cranes. Twenty variables considered attributable to the hoisting time are evaluated. In modeling the hoisting movements, the upward hoisting and downward hoisting of a hoisting cycle are classified as the supply hoisting and the return hoisting. Multiple regression techniques are used for analyzing and building up the models. The hoisting of precast concrete facades and concrete skips are studied. The significance of the variables are discussed and compared. Finally, the paper presents an example of applying the models.