Application of Machine Learning and Multivariate Statistics to Predict Uniaxial Compressive Strength and Static Young’s Modulus Using Physical Properties under Different Thermal Conditions

Khan, Naseer Muhammad; Cao, Kewang; Yuan, Qiupeng; Hashim, Mohd Hazizan Bin Mohd; Rehman, Hamood Ur; Hussain, Sajjad; Emad, Muhammad Zaka; Ullah, Barkat; Shah, Kausar Sultan; Khan, Sajid Aleem

doi:10.3390/su14169901

Cited by 30 publications

(14 citation statements)

References 65 publications

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“…Therefore, the use of artificial intelligence methods can be a good solution to predict FR with a high degree of performance. Additionally, the use of artificial intelligence methods in different fields of mingling and civil engineering indicates the effectiveness of these methods for predicting and optimizing aims [21][22][23][24][25][26][27][28][29][30][31].…”

Section: Of 20mentioning

confidence: 99%

Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines

et al. 2023

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Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce the potential risk of damage. In other words, the minimization of flyrock can lead to sustainability of surroundings environment in blasting sites. To this aim, the present study develops several new hybrid models for predicting flyrock. The proposed models were based on a cascaded forward neural network (CFNN) trained by the Levenberg–Marquardt algorithm (LMA), and also the combination of least squares support vector machine (LSSVM) and three optimization algorithms, i.e., gravitational search algorithm (GSA), whale optimization algorithm (WOA), and artificial bee colony (ABC). To construct the models, a database collected from three granite quarry sites, located in Malaysia, was applied. The prediction values were then checked and evaluated using some statistical criteria. The results revealed that all proposed models were acceptable in predicting the flyrock. Among them, the LSSVM-WOA was a more robust model than the others and predicted the flyrock values with a high degree of accuracy.

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Section: Of 20mentioning

confidence: 99%

Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines

et al. 2023

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“…Also, they found that the density and Schmidt rebound hardness had more related to the UCS than the rock porosity. Khan et al [ 26 ] applied the MLR, ANN, RF, and KNN for predicting the UCS and static E from physical, chemical, and mechanical properties of marble rock namely density, porosity, P-wave velocity, and dynamic E under different thermal conditions. They found that the KNN and RF are reliable approaches to predict both UCS and E. Also, it was found that P-wave velocity has strong correlations with the UCS and E. Based on predictive performance, the RF model was proposed to predict the UCS and E as the best model.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Fereidooni,

Karimi,

Ghasemi

2024

PLoS ONE

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The uniaxial compressive strength (UCS) and elasticity modulus (E) of intact rock are two fundamental requirements in engineering applications. These parameters can be measured either directly from the uniaxial compressive strength test or indirectly by using soft computing predictive models. In the present research, the UCS and E of intact carbonate rocks have been predicted by introducing two stacking ensemble learning models from non-destructive simple laboratory test results. For this purpose, dry unit weight, porosity, P‐wave velocity, Brinell surface harnesses, UCS, and static E were measured for 70 carbonate rock samples. Then, two stacking ensemble learning models were developed for estimating the UCS and E of the rocks. The applied stacking ensemble learning method integrates the advantages of two base models in the first level, where base models are multi-layer perceptron (MLP) and random forest (RF) for predicting UCS, and support vector regressor (SVR) and extreme gradient boosting (XGBoost) for predicting E. Grid search integrating k-fold cross validation is applied to tune the parameters of both base models and meta-learner. The results demonstrate the generalization ability of the stacking ensemble method in the comparison of base models in the terms of common performance measures. The values of coefficient of determination (R2) obtained from the stacking ensemble are 0.909 and 0.831 for predicting UCS and E, respectively. Similarly, the stacking ensemble yielded Root Mean Squared Error (RMSE) values of 1.967 and 0.621 for the prediction of UCS and E, respectively. Accordingly, the proposed models have superiority in the comparison of SVR and MLP as single models and RF and XGBoost as two representative ensemble models. Furthermore, sensitivity analysis is carried out to investigate the impact of input parameters.

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“…Numerous services and sectors have seen significant productivity gains due to ML. Although it is still in its infancy in the construction sector, its application has grown recently to address several difficulties, including concrete technology 12 – 16 and concrete durability 17 – 19 . The other various application in the field of civil and environmental engineering of ML algorithms can be found in the published research work 20 – 24 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

Kumar

Arora

Kapoor

et al. 2023

Sci Rep

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The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

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Application of Machine Learning and Multivariate Statistics to Predict Uniaxial Compressive Strength and Static Young’s Modulus Using Physical Properties under Different Thermal Conditions

Abstract: Keywords: thermal effect prediction model; uniaxial compressive strength; static Young’s modulus; artificial neural network; multilinear regression

Cited by 30 publications

References 65 publications

Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines

Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

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