Four Decades of Computing in Civil Engineering

Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar

Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches' results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics.

Jellyfish Search-Optimized Deep Learning for Compressive Strength Prediction in Images of Ready-Mixed Concrete

Most building structures that are built today are built from concrete, owing to its various favorable properties. Compressive strength is one of the mechanical properties of concrete that is directly related to the safety of the structures. Therefore, predicting the compressive strength can facilitate the early planning of material quality management. A series of deep learning (DL) models that suit computer vision tasks, namely the convolutional neural networks (CNNs), are used to predict the compressive strength of ready-mixed concrete. To demonstrate the efficacy of computer vision-based prediction, its effectiveness using imaging numerical data was compared with that of the deep neural networks (DNNs) technique that uses conventional numerical data. Various DL prediction models were compared and the best ones were identified with the relevant concrete datasets. The best DL models were then optimized by fine-tuning their hyperparameters using a newly developed bio-inspired metaheuristic algorithm, called jellyfish search optimizer, to enhance the accuracy and reliability. Analytical experiments indicate that the computer vision-based CNNs outperform the numerical data-based DNNs in all evaluation metrics except the training time. Thus, the bio-inspired optimization of computer vision-based convolutional neural networks is potentially a promising approach to predict the compressive strength of ready-mixed concrete.