Purpose-To address requirements and specifications of construction project, academics need to build a project classification model. In recent years, project success concept, particularly on large-scale construction projects, has been a controversial issue, especially in developing countries. Hence, in this paper, after introducing a sustainable success index (SSI), a novel method called "rough set approach" had been adopted to induce decision rules and to classify construction projects. The paper aims to discuss these issues. Design/methodology/approach-At first, 20 effective success factors and 15 success criteria based on three pillars of sustainability of economy, society and environment had been categorized. The research data used for analysis had been collected from 26 large-scale construction projects in Iran and five other countries. After collecting data collection, observations had been analyzed and 51 decision rules were generated, and the projects were classified. Eventually, in order to evaluate the performance of the generated rules, confusion matrix was applied, and the model was validated. Findings-The results of the present study show that rough set theory (RST) can be an effective and valuable tool for building expert systems. Practical applications of these results along with limitations and future research are described. Originality/value-Perhaps for the first time, in the present study, a number of large-scale construction projects are classified based on SSI. Applying RST for building rule-based system and classifying projects in construction project area are novel attempts undertaken in this paper. The rules induced in this study can be applied to develop a sustainable success prediction model in the future studies.

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Purpose Successful implementation of infrastructure projects has been a controversial issue in recent years, particularly in developing countries. This study aims to propose a decision support system (DSS) for the evaluation and prediction of project success while considering sustainability criteria. Design/methodology/approach To predict sustainable success factor, the study first developed its sustainable success factors and sustainable success criteria. These then formed a decision table. A rough set theory (RST) was then implemented for rules generation. The decision table was used as the input for the rough set, which returned a set of rules as the output. The generated rulesets were then filtered in fuzzy inference system (FIS), before serving as the basis for the DSS. The developed prediction tool was tested and validated by applying data from a real infrastructure project. Findings The results show that the developed rough set fuzzy method has strong ability in evaluation and prediction of the project success. Hence, the efficacy of the DSS is greatly related to the rule-based system, which applies RST to generate the rules and the result of the FIS was found to be valid via running a case study. Originality/value Use of DSS for predicting the sustainable success of the construction projects is gaining progressive interest. Integration of RST and FIS has also been advocated by the seminal literature in terms of developing robust rulesets for impeccable prediction. However, there is no preceding study adopting this integration for predicting project success from the sustainability perspective. The developed system in this study can serve as a tool to assist the decision-makers to dynamically evaluate and predict the success of their own projects based on different sustainability criteria throughout the project life cycle.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

This study presents a multiobjective energy management model for the connected cophase traction power system (CCTPS). Each traction substation (TSS) includes a power flow controller (PFC), energy storage systems (ESS), wind turbine, and PV modules beside a single-phase traction power transformer. Also, in order to exchange the power between the adjacent TSSs, power transfer controllers (PTCs) are used. The proposed energy management model is formulated as a multistage multiobjective optimization problem with a lexicography approach. In the first stage, the cost of purchased energy is minimized. In the second stage, the independence of the CCTPS from the external grid improved. Finally, minimizing the voltage unbalanced ratio (VUR) of CCTPS is considered as the third stage goal. According to the simulation results, utilizing GAMS optimization software, the proposed model will decrease remarkably VUR and dependency of CCTPS without any increase in operation cost.

This article presents a coordinated operation model for energy management of a multiintegrated energy system based on Mixed-Integer Linear Programing (MILP). The power derived by trains from regenerative braking energy (RBE), during deceleration, is utilised to meet the interconnected energy hubs' (IEHs) demand. The recovered energy is calculated by simulating the motion of the trains in MATLAB software. The electricity and heat demand response (DR) programs are integrated into the proposed model to study their impacts on the operating cost and the carbon emission of the IEH, considering several case studies. Furthermore, the uncertainties of the RBE, photovoltaic power generation, and loads of the IEH are considered by formulating the optimisation problem stochastically through a scenario-based approach. Therefore, a scenario generation and reduction decision-making technique is employed. Finally, the GAMS optimisation software is used to assess the efficiency of the presented MILP model. The simulation results indicate that the total operating cost of the IEH reduced 2.0% and 1.4% in the case studies. Also, the CO 2 emission is decreased by about 0.3% by applying the coordination scheme besides the DR programs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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