Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

Abstract

Not only can waste rubber enhance the properties of concrete (e.g., its dynamic damping and abrasion resistance capacity), its rational utilisation can also dramatically reduce environmental pollution and carbon footprint globally. This study is the world’s first to develop a novel machine learning-aided design and prediction of environmentally friendly concrete using waste rubber, which can drive sustainable development of infrastructure systems towards net-zero emission, which saves time and cost. In this study, artificial neuron networks (ANN) have been established to determine the design relationship between various concrete mix composites and their multiple mechanical properties simultaneously. Interestingly, it is found that almost all previous studies on the ANNs could only predict one kind of mechanical property. To enable multiple mechanical property predictions, ANN models with various architectural algorithms, hidden neurons and layers are built and tailored for benchmarking in this study. Comprehensively, all three hundred and fifty-three experimental data sets of rubberised concrete available in the open literature have been collected. In this study, the mechanical properties in focus consist of the compressive strength at day 7 (CS7), the compressive strength at day 28 (CS28), the flexural strength (FS), the tensile strength (TS) and the elastic modulus (EM). The optimal ANN architecture has been identified by customising and benchmarking the algorithms (Levenberg–Marquardt (LM), Bayesian Regularisation (BR) and Scaled Conjugate Gradient (SCG)), hidden layers (1–2) and hidden neurons (1–30). The performance of the optimal ANN architecture has been assessed by employing the mean squared error (MSE) and the coefficient of determination (R2). In addition, the prediction accuracy of the optimal ANN model has ben compared with that of the multiple linear regression (MLR).

The Use of Artificial Neural Network for Predicting the Thermal Conductivity of Cement Based Mortar with Natural Zeolites

“…The MSE is a metric that tells the distance, or how far apart, are the predicted values from the observed, in this case experimentally determined, values in a dataset. According to earlier studies the number of neurons in the hidden layer is an essential parameter influencing the accuracy of the ANN [15]. A too large number of neurons in the hidden layer may lead to overfitting while a too low number may result in underfitting issues [16].…”

Influence of a Novel Carbon-Based Nano-Material on the Thermal Conductivity of Mortar

“…Although concrete can incorporate a large variety of components, some of them by-products or other industries, e.g., with treated palm oil fuel ash, the use of the ANN in predicting the compressive strength has proven to yield accurate results [16]. Other studies have shown the suitability of using the ANN when it comes to rubberized concrete [17] and concrete using fly-ash as a partial substitute for Portland cement [18]. It should be pointed out that even slight changes in concrete composition may result in different values for the mechanical properties.…”

Prediction of Thermal-Induced Buckling Failures of Ballasted Railway Tracks Using Artificial Neural Network (ANN)

“…Machine learning technique has been widely used in civil and railway applications especially for predicting the properties of materials of track components based on the proportion of the ingredients e.g. recycled concrete [23], rubberised concrete [24] etc. More examples on railway track applications includes track response quantification [25], train weight prediction [26], fault detection [27][28][29][30], railway safety and accident identification [31,32].…”