Machine learning in concrete technology: A review of current researches, trends, and applications

Abstract

Machine learning techniques have been used in different fields of concrete technology to characterize the materials based on image processing techniques, develop the concrete mix design based on historical data, and predict the behavior of fresh concrete, hardening, and hardened concrete properties based on laboratory data. The methods have been extended further to evaluate the durability and predict or detect the cracks in the service life of concrete, It has even been applied to predict erosion and chemical attaches. This article offers a review of current applications and trends of machine learning techniques and applications in concrete technology. The findings showed that machine learning techniques can predict the output based on historical data and are deemed to be acceptable to evaluate, model, and predict the concrete properties from its fresh state, to its hardening and hardened state to service life. The findings suggested more applications of machine learning can be extended by utilizing the historical data acquitted from scientific laboratory experiments and the data acquitted from the industry to provide a comprehensive platform to predict and evaluate concrete properties. It was found modeling with machine learning saves time and cost in obtaining concrete properties while offering acceptable accuracy.

Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks

“…ANN is a powerful mathematical model that can be easily used to predict the targets of any experimental set, including volcanic tuff testing [21]. ANN consists of a set of fully connected neurons; each connection has a weight, and the neurons are arranged in a selected number of layers, as shown in Figure 1-a.…”

“…These methods, while valuable in some cases, often lack precision and reproducibility, resulting in variations in mix-design outcomes. In recent years, researchers have explored the use of ANNs to optimize the mix design of conventional concrete and provide a more systematic and data-driven approach [20,21]. Recent developments in deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), present opportunities to enhance the modeling capabilities and prediction accuracy for concrete mix-design outcomes [21,22].…”

A Comparative Analysis of Compressive and Flexural Strength in Concrete with Partial Cement Replacement using Waste Glass Powder

“…Conversely, low compressive strength may lead to structural failures, com-promising safety (Yuan et al, 2023), necessitating costly repairs, retrofitting and challenges the durability (Girish et al, 2023). On the other hand, flexural strength denotes the material's capability to resist deformation under bending, specifically the maximum tensile stress it can endure without fracturing when subjected to a bending moment (Marí et al, 2015;Gamil, 2023). High flexural strength is crucial for structure stability, resistance to dynamic forces and flexibility, especially in long span structures (Mohaghegh et al, 2017), whereas low flexural strength weaken the structures, reduces the safety and necessitates more maintenance (Ahmed et al, 2016).…”

Prediction of the Dynamic Properties of Concrete Using Artificial Neural Networks

“…Historically, approaches to dynamic properties estimation depended on time-consuming and frequently expensive experimental testing, which may not be practicable in all circumstances. The demand for fast, cost-effective, and exact approaches to estimating these qualities has fueled computational intelligence research, notably Artificial Neural Networks (ANNs) [16,17]. ANNs, a type of machine learning, have emerged as a potential method for learning from data and predicting complicated material behavior, providing an alternative to traditional experimental approaches.…”