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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]

The problem of handling equipment selection plays a significant role in the total cost of a mining project; so that it can affect the activity and continuity of the project and is a strategic problem. In this study, an integrated model based on two fuzzy multi-criteria decision making techniques for handling equipment selection is proposed. The proposed evaluation model is derived from group decision making, fuzzy set theory, analytical hierarchy process (AHP), and Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The fuzzy AHP (FAHP) method is utilized to calculate the relative importance of the evaluation criteria, then, fuzzy TOPSIS (FTOPSIS) is applied for evaluating the feasible handling equipment in order to select the best handling system among a pool of the possible alternatives. The model is applied for a real world case study to demonstrate the capability and effectiveness of the proposed model. To investigate the result sensitiveness to the changes of the criteria weights, a sensitivity analysis is finally conducted. [ABSTRACT FROM AUTHOR]

Tunnels are artificial underground spaces that provide a capacity for particular goals such as storage, under-ground transportation, mine development, power and water treatment plants, civil defence. This shows that the tunnel construction is a key activity in developing infrastructure projects. In many situations, tunnelling projects find themselves involved in the situations where unexpected conditions threaten the continuity of the project. Such situations can arise from the prior knowledge limited by the underground unknown conditions. Therefore, a risk analysis that can take into account the uncertainties associated with the underground projects is needed to assess the existing risks and prioritize them for further protective measures and decisions in order to reduce, mitigate and/or even eliminate the risks involved in the project. For this reason, this paper proposes a risk assessment model based on the concepts of fuzzy set theory to evaluate risk events during the tunnel construction operations. To show the effectiveness of the proposed model, the results of the model are compared with those of the conventional risk assessment. The results demonstrate that the fuzzy inference system has a great potential to accurately model such problems. [ABSTRACT FROM AUTHOR]

Abstract: Road header performance prediction plays a significant role in the successful implementation of a tunneling project; so that, there is a need for accurate prediction of the advance rate of tunneling. However, there is relatively less study on predicting the performance of such machinery by using soft computing techniques although they have some advantages over the other methods. On the other hand, often models applied for road header performance prediction neglect interaction between machine and rock mass parameters. The Takagi–Sugeno (TS) fuzzy system model, one of the most popular fuzzy models, can be applied to solve complex problems by transferring a nonlinear system into a set of linear subsystems. However, in many situations, it is not convenient to identify all the rules; so, using the fuzzy clustering techniques in which the rules are resulted from measured data can be useful and valuable. In this paper, a new model based on the geological and geotechnical site conditions is developed to predict the road header performance. The model is developed using soft computing technique that applies the concept of fuzzy logic to take into account the uncertainty and complexity derived from the interaction between rock properties and road header parameters. The prediction capabilities offered by TS fuzzy model based on subtractive clustering method are demonstrated by using field data of obtained from Tabas coal mine in Iran. [Copyright &y& Elsevier]

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]

Abstract: The problem of Tunnel Boring Machine (TBM) selection has a significant impact on the speed and cost of excavating sector; so that it is a strategic issue. On the other hand, selecting the optimum TBM among a pool of alternatives is a multicriteria decision making (MCDM) problem. In this paper, an evaluation model based on the fuzzy analytic hierarchy process (AHP) and another fuzzy MCDM technique, namely fuzzy technique for order performance by similarity to ideal solution (TOPSIS) is developed to help the tunneling designers in the process of the TBM selection under fuzzy environment where the vagueness and uncertainty are taken into account with linguistic variables parameterized by triangular fuzzy numbers. The fuzzy AHP is applied to form the structure of the TBM selection problem and to determine weights of the evaluation criteria, and fuzzy TOPSIS method is utilized to acquire final ranking. A real world case study is illustrated in order to demonstrate the potential of the proposed model for the TBM selection issue. It demonstrates the effectiveness and capability of the proposed model. [Copyright &y& Elsevier]

Selection of the working strategy is a critical problem and it plays a significant role in the success of organization development. On the other hand, selecting the most appropriate working strategy among a pool of alternatives is a multi-criteria decision making (MCDM) problem. Since every working strategy has its benefits and costs and may bring a company different opportunities and risks, which kind of working strategy is the most appropriate for a company to accomplish is a sophisticated and complex decision with a high degree of uncertainty. Therefore, the current paper proposed an integrated evaluation model based on the analytic network process (ANP) and the complex proportional assessment (COPRAS), to help the decision makers or managers with the selection of proper working strategy in a fuzzy environment where the fuzziness and uncertainties are handled with linguistic terms parameterized by triangular fuzzy numbers (TFNs). In this paper fuzzy ANP (FANP) is utilized to take into account interdependence and dependencies and determine the importance weights of benefit, opportunities, cost and risk (BOCR) factors, and fuzzy COPRAS is applied to rank the alternatives. To show the potential application of the proposed model, a real world application is conducted to illustrate the use of the proposed model for the working strategy selection problem. The results show the capability and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In the post-recession period, the business environment in developing countries is still relatively undesirable. Based on the reports published, the number of closed entrepreneurs are significantly more than the number of those newly established. These statistics also reflect that the development of entrepreneurship faces several challenges. However, a strategic management program must be accurately conducted to effectively control entrepreneurial challenges based on the limitation of time and financial resources. On the other hand, the program should provide an appropriate opportunity for competitiveness by producing better quality goods and services successfully marketed to consumers. The aim of this paper is to propose a robust model with a high potential for evaluation of entrepreneurial challenges in order to identify and control the most critical ones. To achieve the aim, the most critical parameters should be identified to prevent wasting the resources. Likewise, the analytical network process (ANP) method, a branch of multi-criteria decision making (MCDM) techniques, is a powerful tool that can consider all interrelationships and interdependencies between criteria. On the other hand, neutrosophic sets are among the most widespread approaches in coping with vagueness and unknown environment. The methodology employed in this paper uses a combination model based on ANP and neutrosophic sets using sustainable development indicators to prioritize the challenges of entrepreneurship. The results show that the proposed model has a high potential to provide a process for decision-making under a vague environment. [ABSTRACT FROM AUTHOR]

Evaluation of business processes plays a significant role in business development and improvement. Therefore, organizations need a systematic approach to evaluate all the changes through robust and powerful techniques that can formulate the relationship between the available information and the degree of the inherent uncertainty. In this paper, a set of operational variables are defined. Then, the SPSS software package is utilized to validate the gathered data. After that, the variables are categorized by the use of a clustering technique. Finally, five major factors are determined as the most effective components. According to the inherent uncertainty involved in the process of modelling, fuzzy set theory, a powerful mathematical tool is applied to handle the vagueness. In order to construct a knowledge base based on the fuzzy set theory, the linguistic concepts for each variable are defined. Lastly, membership functions are described and a set of fuzzy rules based on input-output parameters are written in MATLAB software environment. To demonstrate the potential application of the proposed approach, a real case study is illustrated. The results reflect the capability and effectiveness of the approach proposed in this paper. [ABSTRACT FROM AUTHOR]

Using the balanced scorecard approach based on sustainable development parameters is a powerful and useful methodology to evaluate the sustainable performance of organization or company. In this paper, a new approach based on sustainability balanced scorecard (SBSC) and multi criteria decision making (MCDM) approaches is developed for evaluating the performance of oil producing companies in Iran. For reflecting the interdependent relationships among factors influencing the problem under consideration, analytical network process (ANP), a branch of the MCDM techniques, is employed. However, using the ANP method for calculating the preference ratings of alternatives is a time-consuming and bothersome process; therefore, COPRAS (COmplex PRoportional ASsessment) technique is adopted to prioritize the feasible alternatives in terms of linguistic variables. Based on this study, the results demonstrate the effectiveness of the proposed model. The performance evaluation model proposed by using a combination of the MCDM methods and the SBSC approach helps authorities to make an attempt for achieving a competitive advantage. [ABSTRACT FROM AUTHOR]

Roof fall is one of the most important problems connected with underground coal mines because it plays a significant role in financial and human losses. Hence, it is essential to accurately predict the roof fall rate for the purpose of controlling, reducing, and/or even eliminating the risk of the involved problems. On the other hand, there are many different parameters that make a considerable impact on the occurring roof rate. Most of these factors are not completely known or measurable. Therefore, the problem of predicting roof fall is vague, sophisticated, and complex. Adaptive neuro-fuzzy inference system (ANFIS) is a powerful and robust tool for modeling linear and non-linear patterns in science and engineering problems. In this paper, the ANFIS system is applied to model the roof fall rate in coal mines. The constructed model uses the subtractive clustering method to generate fuzzy rules based on 109 data of roof performance from US coal mines. The results demonstrate that prediction of roof fall rate by the ANFIS model is satisfactory. [ABSTRACT FROM AUTHOR]

Shafts have critical importance in deep mines and underground constructions. There are several traditional and mechanized methods for shaft sinking operations. Using mechanized excavation technique is an applicable alternative to improve project performance, although impose a huge capital cost. There are a number of key parameters for this selection which often are in conflict with each other and decision maker should seek a balance between these parameters. Therefore, shaft sinking method selection is a multi criteria decision making problem. This paper intends to use the combination of analytical hierarchy process and TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) methods under fuzzy environment in order to select a proper shaft sinking method. A real world application is conducted to illustrate the utilization of the model for the shaft sinking problem in Parvadeh Coal Mine. The results show that using raise boring machine is selected as the most appropriate shaft sinking method for this mine. [ABSTRACT FROM PUBLISHER]

The complexity and dynamics of the executive projects have coped contractors with substantial hazards and losses. Project risk management is a critical tool for authority to improve its performance and secure the success of the organization. However, a number of standards and approaches have been developed to formulate the projects based on their risks. The Elena guideline is a systematic standard developed by Iran Project Management Association. This guideline provides the full cycle of the risk management process. Risk evaluation is the key part of the risk management process. On the other hand, different techniques have been developed to model a risk evaluation problem. Fuzzy inference system is one of the most popular techniques that is capable of handling all types of the uncertainty involved in projects. This paper proposes a three-stage approach based on the fuzzy inference system under the environment of the Elena guideline to cope with the risky projects. Finally, an illustrative example of the risk evaluation is presented to demonstrate the potential application of the proposed model. The results show that the proposed model evaluates the risky projects efficiently and effectively. [ABSTRACT FROM AUTHOR]

Nanotechnology plays a significant role in construction industry. The construction industry has been employed nanomaterials to improve the performance of construction components and the safety of the structure and to reduce the energy consuming and the cost of maintenance. In other words, nanotechnology has a substantial impact on the construction industry. Therefore, it is necessary to identify and evaluate the critical factors of the application of nanotechnology in construction in order to concentrate on the most critical factors. However, several techniques have been developed to prioritize the evaluation criteria. Analytical network process (ANP) technique, a branch of multi criteria decision making (MCDM) methods, is a powerful tool to rank a limited number of criteria. This technique takes into account both tangible and intangible criteria in the process of formulation of a decision making problem. This method is capable of handling all types of independence and dependence relationships. On the other hand, intuitionistic fuzzy set (IFS) is a well-known technique in considering the inherent uncertainty involved in the process of modelling a decision making problem. In this paper, a new model based on the IFS and ANP technique is proposed to evaluate the critical factors of the application of nanotechnology in the construction industry. The results demonstrate that the proposed model has a high potential for taking into account the uncertainty in the form of a three dimension function, including membership, non-membership, and non-determinacy. [ABSTRACT FROM AUTHOR]

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]