The construction industry is plagued by risk and often has suffered poor performance as a result. There are a number of risk management techniques available to help alleviate this, but usually these are based on operational research techniques developed in the 1960s, and for the most part have failed to meet the needs of project managers. In this paper, a hierarchical risk breakdown structure representation is used to develop a formal model for qualitative risk assessment. A common language for describing risks is presented which includes terms for quantifying likelihoods and impacts so as to achieve consistent quantification. The relationships between risk factors, risks and their consequences are represented on cause and effect diagrams. These diagrams and the concepts of fuzzy association and fuzzy composition are applied to identify relationships between risk sources and the consequences for project performance measures. A methodology for evaluating the risk exposure, considering the consequences in terms of time, cost, quality, and safety performance measures of a project based on fuzzy estimates of the risk components is presented.

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (C_c) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict C_c through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate C_c. This study presents a novel prediction model for the C_c of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R²), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R², RMSE, and MAE compared to the other models.

The construction industry consistently suffers from poor project performance due to a lack of formalized risk management procedures. A proliferation of techniques are available to support project risk management, but these have failed to meet the needs of project managers. Communication of risks tends to be poor, incomplete, and inconsistent. Part of the problem is a lack of common terms and descriptions in which risks and remedial measures may be identified, assessed, analyzed, and effectively communicated throughout the supply chain. Another part is the reliance on quantitative techniques, which are not always appropriate for use in project risk management. This paper presents a consistent methodology for construction project risk management, including a generic process model and underlying information model, common language for describing risks and remedial actions, fuzzy knowledge representation model to support quantitative risk analysis, and prototype software implementation. It is hoped that the me...