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Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1) artificial intelligence, (2) statistical methods, and (3) analytical methods. In this paper, the multivariate regression (MVR) method, which is one of the most popular linear models, and the artificial neural network (ANN) method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Roof fall is one of the most important hazards connected with underground coal mines, because significantly affects financial and human losses. On the other hand, according to different parameters make a significant impact on roof fall and these factors are ill-defined or even immeasurable, this problem is an uncertain and complex issue. Fuzzy logic is a useful tool to handle the existing uncertainty to be more adapted to the real world problems. In this paper, fuzzy inference system (FIS) is applied to predict roof fall rate more accurate, precise, and sure for controlling, mitigating, and/or even eliminating the risk of roof fall. The model utilizes subtractive clustering method to generate fuzzy rules based on 109 data of roof performance from US coal mines. The established model is evaluated by testing dataset based on three indices, including coefficient of determination (R 2), mean absolute error (MAE), and root mean square error (RMSE). The results derived from the FIS model in comparison with artificial neural network (ANN) and multivariate regression (MVR) model demonstrate that prediction of roof fall rate by the FIS model is more accurate and satisfied. [Copyright &y& Elsevier]

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength. [ABSTRACT FROM AUTHOR]