A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks

Asare, Edmund Nana; Affam, M.; Ziggah, Yao Yevenyo

doi:10.1007/s40808-023-01717-2

Cited by 8 publications

(1 citation statement)

References 68 publications

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“…Multivariate Adaptive Regression Splines (MARS), is an algorithm for complex non-linear regression problems [41]. Asare et al [42] used a multivariate adaptive regression spline for the prediction of the uniaxial compressive strength of rocks. Using statistical analyses Arthur et al [43] revealed that the MARS produced the best performance and can successfully model and predict blast-induced ground vibration.…”

Section: Prediction Of Aop By Marsmentioning

confidence: 99%

Mine Induced Airblast prediction: An Application of Chaos Game Optimization based soft computing approaches

Hosseini,

Taiwo,

Fissha

et al. 2023

Preprint

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Air overpressure, often known as AOp, is one of the unfavourable effects of galena blasting. It has high damage potential to structural buildings, ecosystem, and occasionally endanger mine workers due to the ore characteristics. The efficiency of most available techniques to manage this mine challenge is site specific and sometimes limited by poor prediction accuracy. In the current study, several deep and machine learning approaches have used to develop blast-induced AOP prediction models as a way forward to the recent gap. These techniques include, Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Random Forest (RF), and Multivariate Adaptive Regression Splines (MARS). To achieve the research aim, 90 measured field data sets were monitored and collected from the Anguran open-pit lead-and-zinc mine (Iran). To improve the proposed model performance, chaos game optimisation (CGO) as a nature-inspired algorithm was adopted. The optimized models were compared with each other and as well with the ordinary models to determine the model with the best prediction accuracy. The results show that, in terms of accuracy levels, the performance of hybrid algorithm approach is superior to that of single based models. The LSTM-CGO model, out of the 10 proposed models, has the highest prediction accuracy statistically. This study demonstrated how well deep learning techniques provide solution to safe and environmental friendly mining operation. The proposed soft computing models are applicable as a tool to forecast AOP in surface mine blasting operation as a pre-blast design decision making reference.

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Section: Prediction Of Aop By Marsmentioning

confidence: 99%

Mine Induced Airblast prediction: An Application of Chaos Game Optimization based soft computing approaches

Hosseini,

Taiwo,

Fissha

et al. 2023

Preprint

View full text Add to dashboard Cite

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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Modeling of kappa factor using multivariate adaptive regression splines: application to the western Türkiye ground motion dataset

Kurtulmuş,

Yerlikaya–Özkurt,

Askan

2024

Nat Hazards

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The recent seismic activity on Türkiye’s west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North–South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region–specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three–component strong–ground motion data from accelerometer sites with known VS30 values throughout western Türkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well–known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites.

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A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks

Cited by 8 publications

References 68 publications

Mine Induced Airblast prediction: An Application of Chaos Game Optimization based soft computing approaches

Mine Induced Airblast prediction: An Application of Chaos Game Optimization based soft computing approaches

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Modeling of kappa factor using multivariate adaptive regression splines: application to the western Türkiye ground motion dataset

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