Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria‐Based Concrete

Abstract

Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network

“…However, due to the costly nature of such experimentations and a lack of generalized accurate empirical models, ANN models have been developed for different conditions and compositions. The compressive strength of conventional concrete [37][38][39][40], high-strength concrete [41][42][43], and concrete with silica fume [44,45], volcanic scoria [46], fly ash [45,47,48], palm oil fuel ash [21,49], clay bricks [50], limestone filler [51], and waste quartz mineral dust [52] have been predicted using ANN. Safiuddin et al (2016) [49] developed an ANN model to predict the compressive strength of the SCC containing up to 30 wt% of POFA.…”

Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather

“…These have also been used by researchers for developing prediction models for reservoir discharge calculations [17], municipal solid waste management [18], rainfall prediction [19], sediment transport [20], and rock strength prediction [21]. al-Swaidani and Khwies [22] developed an ANN model to investigate five parameters for predicting the properties of concrete. These parameters include the curing period, w/c, volcanic scoria, and super plasticizer content.…”

A Novel Machine Learning Model to Predict the Photo-Degradation Performance of Different Photocatalysts on a Variety of Water Contaminants

“…ML approach is faster, cheaper, and more flexible than experiments. An artificial neural network (ANN) is an ML model that has been widely used to predict the properties of materials, ranging from polymers, metals, ceramics to composite materials [16][17][18][19][20][21]. It has also been explored to assist the accelerated discovery and design of novel photocatalysts [22,23] and to predict the photocatalytic performance of a photocatalyst [24][25][26][27][28].…”