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The concrete manufacturing supply chain is one of the most carbon-intensive systems in the construction industry. Decision-making based on a multi-objective approach that accommodates the environmental impacts of concrete manufacturing has been rarely investigated. To fill this gap, a sustainable closed-loop supply chain (SCLSC) model was conceived to capture the effect of different circular systems. The proposed model consists of different sub-systems including customers, suppliers, and manufacturing and recycling stations. An evolutionary-based algorithm named non-dominated sorting genetic algorithm was incorporated to solve the solutions. The SCLSC model was implemented on two scenarios based on in-house or outside recycling plants to minimize the quarry of natural resources, the transportation cost, and the greenhouse gas (GHG) emissions. General Algebraic Modeling System (GAMS) programming software was used to validate the model and to verify the obtained results from the model. The Pareto solution results show that larger incorporation of recycled aggregates in concrete production can lower the excavation of quarries. Furthermore, although a decision for incorporating a green (net-zero GHG) cement contributes to reducing the GHG emissions of the supply chain, the transportation distance determines whether the demand would be supplied from the green cement source. The provision of an in-house concrete recycling unit can reduce the GHG emissions of the supply chain by 14% while the cost and virgin aggregate demand increase by 24% and 16%, respectively compared to a system that has an outside recycling plant. The validation process shows that the GAMS software needs additional steps to conduct the multi-objective Pareto solutions. Similar values were obtained from the proposed algorithm compared to the outcome of GAMS. The computational results prove the capability of the proposed SCLSC model and the applicability of the implemented solution approach. This approach can facilitate the decision-making process due to its effectiveness in improving customers’ economic motivations and in reducing the environmental impacts.

Project planning and scheduling are one of the most important issues in construction engineering and management. It is being crucial to progress developed countries. One of the major challenges in construction project management is time and cost management. The traditional view lonely is not able to meet the needs of this field. Therefore, the use of modern management approaches can be greatly helpful. Time and cost are among the important objectives of each project. Cost and time trade-off is among the major important issues in projects planning and control and the main of solving this problem is actually analysis of interaction of different types of project costs and time of the project. In this paper, a time and cost trade off project scheduling problem under budget constraint is studied. For this purpose, a meta-heuristic genetic algorithm is developed to find the optimal solution in MATLAB. Completion time sensitivity analysis is done according to different budget level. In order to validate proposed algorithm, problem is solved by GAMS and the outputs between them are compared. The results show that proposed meta heuristic algorithm is able to solve problem optimally so that differences between samples solution were zero in different budget level.

The unique fresh and hardened properties of high-performance self-consolidating concrete (HPSCC) led to an extensive application of this mixture in high-rise buildings. In this paper, the elevated temperature resistivity of 19 HPSCC mixtures incorporating binary and ternary blends of fly ash, silica fume, natural zeolite, and metakaolin was investigated. Changes in mass, compressive strength and ultrasonic pulse velocity (UPV) of the mixtures were measured at different temperatures (20, 300, 500, and 700 °C). A life cycle assessment (LCA) was also employed to explore the environmental performance of the mixtures. The test results revealed that in ambient temperature, ternary mixtures incorporating natural zeolite and fly ash or natural zeolite and metakaolin have lower compressive strength than that of the control mixture. The residual compressive strengths of fly ash-silica fume-incorporated mixture was similar to those in binary mixtures. The UPV test results revealed a larger than 50% reduction in transition velocity when the temperature was above 500 °C, and there is a strong association between the UPV and compressive strength test results of the mixtures at different temperatures, but the correlation decreased inversely proportional to the exposure temperature. Among the ternary mixtures, those mixtures that incorporate natural zeolite indicate the most significant mass loss after exposing to elevated temperature. The environmental results indicate that the substitution of pozzolanic materials with Portland cement may not always be beneficial. The ecosystem quality results of binary fly ash mixtures were larger than the control mixture due to the extensive transportation distance of import. In addition, metakaolin binary mixture exposes larger damage to the resources. Silica fume-incorporated mixtures had significant damage to human health. The ternary blended mixtures can be a remedy to obtain the optimized fire-resistant results and to compensate the environmental burdens of the inferior pozzolan in all the damage categories.

In the field of pavement engineering, the determination of the mechanical characteristics is one of the essential process for reliable material design and highway sustainability. Early determination of mechanical characteristics of pavement is highly essential for road and highway construction and maintenance. Tensile strength (TS), compressive strength (CS) and flexural strength (FS) of roller compacted concrete pavement (RCCP) are very crucial characteristics as they are necessitated for many data from mixture proportions as input variables. In this research, the classification-based regression models named Random Forest (RF), M5rule model tree (M5rule), M5prime model tree (M5p) and Chi-square Automatic Interaction Detection (CHAID) are developed for simulation of the mechanical characteristics of RCCP. A comprehensive and reliable dataset comprising 621, 326 and 290 data records for CS, TS and FS experimental cases extracted from several open sources over the literature. The mechanical properties are developed based on influential inputs combination that processed using Principle Component Analysis (PCA). The applied PCA method as feature selection is specified that volumetric/weighted content forms of experimental variables (e.g., coarse aggregate, fine aggregate, supplementary cementitious materials, water and binder) and specimens’ age are the most effective inputs to generate the better performances. Several statistical metrics are measured to evaluate proposed classification-based regression models. RF model revealed an optimistic classification capacity of the CS, TS and FS prediction of the RCCP in comparison with the CHAID, M5rule, and M5p models. The research is extended for the results verification using Monte-carlo model for the uncertainty and sensitivity of variables importance analysis. Overall, the proposed methodology indicated a reliable soft computing model that can be implemented for the material engineering construction and design.

In this study, five different Artificial Intelligence (AI) models, Multivariate Adaptive Regression Splines (MARS), M5P Model Tree (M5P-MT), Least Square Support Vector Machines (LS-SVM), Multilayer Perceptron Neural Network (MLP-NN) and Multiple Linear Regression (MLR) have been developed to predict the Compressive Strength (CS) and Ultrasonic Pulse Velocity (UPV) of Fiber Reinforced Concrete (FRC) incorporating nano silica. Experimental results from 175 and 132 concrete samples with different mixture proportions were collated, respectively, to develop the models for CS and UPV. Standard statistical performance evaluation measures such as the Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Percentage of Error (MAPE), Performance Index (PI), Average Absolute Error (AAE), Standard Deviation (SD) and mean (M) were used to evaluate proposed models in training and testing stages. The MARS model using normalized input data performed better compared to LS-SVM, M5P-MT, MLP-NN and MLR in the prediction of both UPV and CS. The robustness of the developed AI predictive models was verified through external validations and the results indicate that proposed models are robust and they provide accurate predictions of CS and UPV.

The simultaneous reduction of the project cost and time has paramount importance in today's competitive world; however, it is necessary to balance the project time and cost because of the reduction asymmetry of the two factors in a project. How to balance the cost and time parameters in managing construction projects is also critical and there have always been some attempts made to provide different approaches to control it. Given the immense importance of considering resource constraints for project scheduling problems, and the proximity of the study conditions to the real world, resource constraints were also included. In a project, project managers need to be aware of resource constraints. As resource constraint scheduling problem is considered NP-hard, the metaheuristic models were developed in this paper in order to obtain results contributing to project managers’ decision-making. For this purpose, a non-dominated sorting genetic algorithm method was developed to optimize a time-cost trade-off problem. Furthermore, to solve a multi-project scheduling problem, the critical chain method was used. In order to evaluate the performance of the model, the developed model was first studied in a small scale and then simultaneous projects with 7, 8 and 10 tasks were planned. Because resource availability is essential in such problems, after solving the proposed model, a sensitivity analysis was performed for daily resources and the results were discussed. Results shows the ability of the proposed model and methodology to optimize the time-cost tradeoff considering resource constraints in sample problems. Solutions obtained showed that in some cases of scheduling without this algorithm, resource consumption exceeded above resource availability. After solving the model by proposed algorithm, resource allocation is implemented considering resource constraint. This model determined these resources as crucial and helped managers to control them.

The purpose of this study is to compare the weighted linear combination (WLC) and fuzzy logic (FL) models in identifying the most suitable location for a water reservoir in the area of Batu Pahat, Johor, Malaysia. First of all, parameters important to a water reservoir for the studied area were identified. Then maps of the study area were prepared and integrated. The main criteria selected for this study are pipe line, elevation, rive, land use, road network, water supply network, and slope. Suitable locations for the water reservoir were selected using each model. The results of this study indicated that the FL method in the early stages had better decision-making powers for locating water reservoir sites when compared to the WLC method. The appropriate places for water reservoir construction based on two models were located to the northwest and west of the study area. The two models are validated using the Kappa index. According to the results of the validation method, the map produced by the FL method exhibits satisfactory properties in the study area. Thus, it can be concluded that the results derived from two models (WLC and FL) integrated in ArcGIS can be a useful tool in GIS analysis for the determination of suitable locations for water reservoir in the study area.

Accurate prediction of compressive strength (fc) is one of the crucial problems in the concrete industry. In this study, novel self-adaptive and formula-based model called Multivariate Adaptive Regression Splines optimized using Water Cycle Algorithm (MARS-WCA) is proposed for modeling fc based on mixture proportion. The proposed predictive model is validated against several benchmark models including Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Support Vector Regression (SVR) and standard MARS model. 418 experimental datasets are collected from the open-source literatures to calibrate and validate the computational intelligence models. The best subset regression procedure is conducted based on different forms of combinations using Mallow’s coefficient to specify the effective variables influencing the fc of Foamed Cellular Lightweight Concrete (FCLC). The applied MARS-WCA model is evaluated with the external validation and uncertainty analysis. It is found that foam, sand, binder, water to cement ratio, sand to cement ratio and age of specimens are the most essential predictors to provide the minimum Mallow’s coefficient value. In quantitative terms, MARS-WCA attained (NSE = 0.938) and that reporting an enhancement of FCLC compressive strength prediction capability over the MLR, ANN, radial basis function-SVR, polynomial-SVR and MARS by 39.4%, 9.2%, 9.6%, 41.7% and 4.7% in term of Nash-Sutcliffe efficiency indicator. Overall, the proposed self-adaptive MARS-WCA model demonstrated a robust and significant data-intelligence mode for FCLC compressive strength prediction compared with the benchmark models and experimental formulations.

The town bridges are among the most important structural elements that decrease the traffic problems. The bridge management system is one of the significant components of the transport management system. Allocating appropriate funds for repairing and maintaining the bridges for the governmental institutions is needed. Therefore, in big cities with multiple bridges and limited funds, the need to investigate the bridges for their repair, maintenance, enforcement, resistant making, and substituting the risky ones increase. In this study, for the first time, through using two methods of decision making, the FUZZY aggregate simple weight (FSAW) and the analytical network procedure (ANP), the risk ability of various bridges in Babolsar in a case wise approach was appraised and it became evident that the second bridge of this city has a high risk compared to the other bridges of the city. Also, in this paper, for the first time, the risk appraisal of the bridges in the exploitation mode was investigated. Examining the effective measures on the risk of the bridge along with the findings of prior research showed that earthquake, flood, traffic load, bridge abrasion and the life span of the bridge are the most important measures of appraising the risk of bridges in the exploitation mode. The bridges in Babolsar were investigated through using the two methods of decision making, ANP and FSAW. It was found that the first bridge of this city has the most probability of destruction and should be given more attention.

The city of Saqqez has a population of 140,000 people, making it one of the largest cities in Iran. Population growth, consumerism, and change in eating habits, such as the increased use of packaged products, is causing the accumulation of waste in this city to increase. In this study, the selection of a waste landfill site for Saqqez focused on 13 layers of geography information that was used by the IDRISI and Arc GIS software. Different models of the analytic multi-criteria decision-making process, such as an analytical hierarchy process (AHP), weighted linear combination (WLC), and Boolean logic, were used to manage layers to establish specific databases for urban waste landfills. Satellite images (Landsat ETM+ and SPOT 5), proposed sites and a land use map of the study area were also used. The results of this study indicated that two methods (AHP and WLC) in the early stages had better decision-making powers for locating landfill sites when compared to Boolean logic. Overlapping and compounding the similarities between these models in Arc GIS software, a 74-ha site was found. This site will be able to accept 130 tons of waste per day for the next 20 years.