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Despite broad improvements in construction management, claims still are an inseparable part of many con-struction projects. Due to huge cases of claim in construction industry, this study argues that claim management is a significant factor in construction projects success. In this study, the most possible causes of these emerging claims are identified and statistically ranked by Probability-Impact Matrix. Subsequently, by classifying claims in different cases, the most important ones are ranked in order to achieve a better understanding of claim management in each project. In this regard, a new index is defined, being able to be applied in a variety of projects with different time and cost values, to calculate the amount of possible claims in each project along with related ratios with respect to the cost and time of each claim. This study introduces a new model to predict the frequency of claims in construction projects. By using the proposed model, the rate of possible claims in each project can be obtained. This model is validated by applying it into fitting case studies in Iran construction industry.

Developing a preci se and accurate model of gold price is critical to assets management because of its unique features. In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artifi cial neural network (ANN) model have been used for modeling the gold price, and compared with the traditional statistical model of ARIMA (autoregressive integrated moving average). The three performance measures, the coeffi cient of determination (R 2 ), root mean squared error (RMSE), mean absolute error (MAE), are utilized to evaluate the performances of different models developed. The results show that the ANFIS model outperforms other models (i.e. ANN and ARIMA model), in terms of different performance criteria during the training and validation phases. Sensitivity analysis showed that the gold price changes are highly dependent upon the values of silver price and oil price.