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Underground spaces having features such as stability, resistance, and being undetected can play a key role in reducing vulnerability by relocating infrastructures and manpower. In recent years, the competitive business environment and limited resources have mostly focused on the importance of project management in order to achieve its objectives. In this research, in order to find the best balance among cost, time, and quality related to construction projects using reinforced concrete in underground structures, a multi-objective mathematical model is proposed. Several executive approaches have been considered for project activities and these approaches are analyzed via several factors. It is assumed that cost, time, and quality of activities in every defined approach can vary between compact and normal values, and the goal is to find the best execution for activities, achieving minimum cost and the maximum quality for the project. To solve the proposed multi-objective model, the genetic algorithm NSGA-II is used.

Ever since their presentation in the late 80s, self-compacting concrete (SCC) has been well received by researchers. SCC can flow under their weight and exhibit high workability. Nonetheless, their nonlinear behavior has made the prediction of their mix properties more demanding. Furthermore, the complex relationship between mixed proportions and rheological and mechanical properties of SCC renders their behavior prediction challenging. Soft computing approaches have been shown to optimize and reduce uncertainties, and therefore in this paper, we aim to address these challenges by employing artificial neural network (ANN) models optimized using the grey wolf optimizer (GWO) algorithm. The optimized model proved to be more accurate than genetic algorithms and multiple linear regression models. The results indicate that the four most influential parameters on the compressive strength of SCC are the cement content, ground granulated blast furnace slag, rice husk ash, and fly ash.

The goal of the present research is to evaluate three bivariate models of the frequency ratio, Shannon entropy (SE) and evidential belief function in the spatial prediction of groundwater at the Sero plain located in west Azerbaijan, Iran. In the first phase, well locations with groundwater yields >11 m 3 /hr were identified (75 well locations). Ten groundwater conditioning factors affecting the occurrence of groundwater, namely, altitude, slope degree, curvature, slope aspect, rainfall, soil, land-use, geology and distance from the fault and the river, were selected for modelling. Finally, the groundwater potential map results were drawn from three implemented models and they were validated using testing data by area under the receiver operating characteristic curve (AUC). The AUCs of these models were 0.84, 81 and 85%, respectively. The results of the current study demonstrated that these models could be successfully employed for spatial prediction modelling. Moreover, the results of the SE model demonstrated that the most and the least important factors in groundwater occurrences in the area under study were altitude, curvature and rainfall, respectively. The results of this study are helpful for the Regional Water Authority of Urmia and the decision makers to comprehensively assess the groundwater exploration development and environmental management in future planning.

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