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Nowadays limestone powder and blast furnace slag (BFS) are widely used in concrete as blended materials in cement. The replacement of Portland cement by limestone powder and BFS can lower the cost and enhance the greenness of concrete, since the production of these two materials needs less energy and causes less CO 2 emission than Portland cement. Moreover, the use of limestone powder and BFS improves the properties of fresh and hardened concrete, such as workability and durability. Engineered cementitious composites (ECC) is a class of ultra ductile fiber reinforced cementitious composites, characterized by high ductility, tight crack width control and relatively low fiber content. The limestone powder and BFS are used to produce ECC in this research. The mix proportion is designed experimentally by adjusting the amount of limestone powder and BFS, accompanied by four-point bending test and uniaxial tensile test. This study results in an ECC mix proportion with the Portland cement content as low as 15% of powder by weight. This mixture, at 28 days, exhibits a high tensile strain capacity of 3.3%, a tight crack width of 57 lm and a moderate compressive strength of 38 MPa. In order to promote a wide use of ECC, it was tried to simplify the mixing of ECC with only two matrix materials, i.e. BFS cement and limestone powder, instead of three matrix materials. By replacing Portland cement and BFS in the aforementioned ECC mixture with BFS cement, the ECC with BFS cement and limestone powder exhibits a tensile strain capacity of 3.1%, a crack width of 76 lm and a compressive strength of 40 MPa after 28 days of curing.

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The shear strength of rockfill materials (RFM) is an important engineering parameter in the design and audit of geotechnical structures. In this paper, the predictive reliability and feasibility of random forests and Cubist models were analyzed by estimating the shear strength from the relative density, particle size, distribution (gradation), material hardness, gradation and fineness modulus, and confining (normal) stress. For this purpose, case studies of 165 rockfill samples have been applied to generate training and testing datasets to construct and validate the models. Thirteen key material properties for rockfill characterization were selected to develop the proposed models. Validation and comparison of the models have been performed using the root mean square error (RMSE), coefficient of determination (R2), and mean estimation error (MAE) between the measured and estimated values. A sensitivity analysis was also conducted to ascertain the importance of various inputs in the prediction of the output. The results demonstrated that the Cubist model has the highest prediction performance with (RMSE = 0.0959, R2 = 0.9697 and MAE = 0.0671), followed by the random forests model with (RMSE = 0.1133, R2 = 0.9548 and MAE= 0.0665), the artificial neural network (ANN) model with (RMSE = 0.1320, R2 = 0.9386 and MAE = 0.0841), and the conventional multiple linear regression technique with (RMSE = 0.1361, R2 = 0.9345 and MAE = 0.0888). The results indicated that the Cubist and random forests models are able to generate better predictive results of the shear strength of RFM than ANN and conventional regression models. The Cubist model was considered to be more promising for interpreting the complex relationships between the influential properties of RFM and the shear strengths of RFM to some extent, which can be extremely helpful in estimating the shear strength of rockfill materials.

Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R2), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R2 of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R2 of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively.