Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

Abstract

High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods

“…However, in the global space of scientific research and in practice, there is a deficit, expressed in a certain conservatism of the construction industry and the slow pace of the introduction of artificial intelligence methods and other innovative methods in this industry [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ].…”

“…Backpropagation neural network (BPNN) models have been used to predict the torsional strength of reinforced concrete beams [ 22 ], the unconfined compressive strength of high-strength concrete [ 23 ] showed high accuracy, and the resulting BAS-BPNN model with the beetle antennae search (BAS) algorithm outperformed widely used machine learning models such as SVM, random forest (RF), K-nearest neighbors (KNN), logistic regression (LR), and multiple-linear regression (MLR) [ 23 , 24 ].…”

“…All the above-mentioned machine learning methods and neural network models have been applied to a wide range of materials, such as heavy concrete without additives [ 9 , 27 , 28 ], concrete with the addition of industrial and agricultural waste ash [ 21 ], slag [ 16 ], eggshell powder [ 12 ], microsilica [ 20 ], recycled concrete aggregate [ 18 , 32 ], ceramic waste [ 11 ], reinforced concrete with the addition of carbon nanotubes/nanofibers, [ 10 ] high-strength concrete [ 23 , 31 ], fiber-reinforced concrete [ 29 ], reinforced concrete (columns, beams, slabs) [ 17 , 19 , 22 , 30 ], and geopolymer concrete [ 13 ], as well as porous cement pastes [ 33 ], soil with cement [ 14 ], and metals [ 15 ].…”

Scour Detection with Monitoring Methods and Machine Learning Algorithms—A Critical Review

“…Multivariate linear regression seeks for the relationship between the variable to be analyzed and its independent variables, and applied for prediction purposes [86]. Trial-error method [87] and beetle antennae search [87,88] are generally preferred when the hyperparameters of back propagation neural network models are tuned. Support Vector Machines by Vladimir Vapnik navigate input vectors from primary feature space to a feature space which is multi-dimensional via a Kernel function [89].…”

Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber

“…The ANN model is essentially a neural network, consisting of an input layer, output layer, and hidden layer(s). As illustrated in Figure 1, each neuron yields the ability of a processing unit, merging information from the former layer to transport the combination to the subsequent nodes [46]. The following equation presents the neuron connection between upper and lower layers in the mathematical version.…”