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To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimize environmental pollution. RCA does not gain more attraction because of the availability of adhered mortar on its surface, which poses a harmful effect on the properties of concrete. However, a suitable mix design for RCA enables it to reach the targeted strength and be applicable for a wide range of construction projects. The targeted strength achievement from the proposed mix design at a laboratory is also a time-consuming task, which may cause a delay in the construction work. To overcome this flaw, the application of supervised machine learning (ML) algorithms, gene expression programming (GEP), and artificial neural network (ANN) was employed in this study to predict the compressive strength of RCA-based concrete. The linear coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to investigate the performance of the models. The k-fold cross-validation method was also adopted for the confirmation of the model’s performance. In comparison, the GEP model was more effective in terms of prediction by giving a higher correlation (R2) value of 0.95 as compared to ANN, which gave a value of R2 equal to 0.92. In addition, a sensitivity analysis was conducted to know about the contribution level of each parameter used to run the models. Moreover, the increment in data points and the use of other supervised ML approaches like boosting, gradient boosting, and bagging to forecast the compressive strength, would give a better response.

The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R2 equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. The lesser values of the errors, MAE, MSE, and RMSE of the boosting technique give 1.69 MPa, 4.16 MPa, and 2.04 MPa, respectively, indicating the high accuracy of the boosting algorithm. However, the statistical check of the errors (MAE, MSE, RMSE) and k-fold cross-validation method confirms the high precision of the boosting technique. In addition, the sensitivity analysis was also introduced to evaluate the contribution level of the input parameters towards the prediction of CS of GPC. The better accuracy can be achieved by incorporating other ensemble ML techniques such as AdaBoost, bagging, and gradient boosting.

Masonry structures in historical sites are deteriorating due to ageing and man-made activities. Regular inspection and maintenance work is required to ensure the structural integrity of historic structures. The inspection work is typically carried out by visual inspection, which is costly and laborious, and yields to subjective results. In this study, an automatic image-based crack detection system for masonry structures is proposed to aid the inspection procedure. Previous crack detection systems generally involve the extraction of hand crafted features, which are classified by classification algorithms. Such approach relies heavily on feature vectors and may fail as some hidden features may not be extracted. In this study, we propose a crack detection system which combines deep Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). CNN is used in extracting features from RGB images and SVM is used as an alternative classifier to a softmax layer to enhance the classification ability. A dataset containing images of cracks from masonry structures was created using a digital camera and an unmanned aerial vehicle from historical sites. The images were used for training and validating the proposed system. It is shown that the combined CNN and SVM model performs better than the model using CNN alone with the detection accuracy of approximately 86% in the validation images. It is also shown that the system can be used to detect cracks automatically for the images of masonry structures, which is useful for inspection of heritage structures.

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