Machine learning-based models for the concrete breakout capacity prediction of single anchors in shear

Olalusi, Oladimeji B.; Spyridis, Panagiotis

doi:10.1016/j.advengsoft.2020.102832

Cited by 37 publications

(23 citation statements)

References 26 publications

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“…In Equation 4, it is also apparent that the anchor diameter and stiffness have some influence on the shear failure load. This influence is estimated to be in the range of 20%, based on a recalculation of the anchor configurations in an extensive experimental database discussed in [29].…”

Section: Analogies To Shear Concrete Edge Failurementioning

confidence: 99%

“…, is the mean shear breakout resistance. In [29], the applicability using GPR and SVR techniques to predict the concrete cone breakout capacity of single anchors loaded in shear is investigated. The predictive effi-…”

Section: Analogies To Shear Concrete Edge Failurementioning

confidence: 99%

“…nom is the outside diameter of the anchor; f cm is the mean concrete cylinder compressive strength; l f is the influence length of the anchor loaded in shear; c 1 is the concrete edge distance; V 0 Rm,c is the mean shear breakout resistance. In [29], the applicability using GPR and SVR techniques to predict the concrete cone breakout capacity of single anchors loaded in shear is investigated. The predictive efficiency of the algorithms is also compared with that of results from various nonlinear regression models currently proposed as design equations, as well as results from ANN algorithms by other researchers.…”

Section: Analogies To Shear Concrete Edge Failurementioning

confidence: 99%

“…Recent investigations present soft computing techniques such as the Gaussian Process Regression (GPR) [ 25 , 26 ], and the Support Vector Regression (SVR) [ 10 , 27 , 28 ] with very high efficiency. This is discussed by [ 29 ], which concluded that both modelling techniques deliver superior predictive accuracy than ANNs and semi-empirical models proposed in current design standards, which are based on nonlinear statistical regression (NR) [ 30 , 31 , 32 ]. Besides SVR and GPR, other ML techniques including ensemble models (including random forests), Adaptive neuro-fuzzy inference systems, Bayesian networks, have been studied as possible candidates for efficient predictive models.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Modelling for Concrete Failure at Anchorages Using Machine Learning Techniques

Spyridis

Olalusi

2020

Materials

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Anchorage to concrete plays a significant role in various aspects of modern construction. The structural performance of anchors under direct tensile load can lead to failure by concrete cone breakout. Concrete related failure modes are quasi-brittle, and as such, they may develop without prior warning indications of damage, while it also exposes the bearing component to damage propagation. As such, an adequate reliability assessment of anchors against concrete cone failure is of high importance, and improved precision and minimisation of uncertainty in the predictive model are critical. This contribution develops predictive models for the tensile breakout capacity of fastening systems in concrete using the Gaussian Process Regression (GPR) and the Support Vector Regression (SVR) machine learning (ML) algorithms. The models were developed utilising a set of 864 experimental anchor tests. The efficiency of the developed models is assessed by statistical comparison to the state-of-practice semi-empirical predictive model, which is embedded in international design standards. Furthermore, the algorithms were evaluated based on a newly introduced Model Explainability concept based on Analogous Rational and Mechanical phenomena (MEARM). Finally, a discussion is provided regarding the developed ML models’ suitability for use as General Probabilistic Models in a reliability framework.

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Section: Analogies To Shear Concrete Edge Failurementioning

confidence: 99%

Section: Analogies To Shear Concrete Edge Failurementioning

confidence: 99%

Section: Analogies To Shear Concrete Edge Failurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive Modelling for Concrete Failure at Anchorages Using Machine Learning Techniques

Spyridis

Olalusi

2020

Materials

Self Cite

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“…A novel self-training hierarchical prototype-based approach for semi-supervised classification was introduced by Gu [13]. Two machine learning models, i.e., a Gaussian process regression and SVM model, for predicting the concrete breakout capacity of single anchors in shear were proposed by Olalusi et al [14]. A hybrid SVM method for two-channel interleaved Vienna rectifier was developed by Wang et al, [15] to reduce the harmonics distortion and current ripple.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

et al. 2020

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For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm. Firstly, based on vibration signals of the bearing, the different p-boxes are obtained and fused using the evidence theory. Then, the different bearing p-boxes can be classified by adopting SVM model; the GA algorithm is considered to optimize key parameters of the SVM model, i.e., GA-SVM. Finally, experimental results show that total recognition rate of this method is better than that of the traditional feature extraction method, which demonstrates the effectiveness of the current method.

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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Machine learning-based models for the concrete breakout capacity prediction of single anchors in shear

Cited by 37 publications

References 26 publications

Predictive Modelling for Concrete Failure at Anchorages Using Machine Learning Techniques

Predictive Modelling for Concrete Failure at Anchorages Using Machine Learning Techniques

Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

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