Neural Network-Based Prediction Model for the Stability of Unlined Elliptical Tunnels in Cohesive-Frictional Soils

Sirimontree, Sayan; Keawsawasvong, Suraparb; Ngamkhanong, Chayut; Seehavong, Sorawit; Sangjinda, Kongtawan; Jearsiripongkul, Thira; Thongchom, Chanachai; Nuaklong, Peem

doi:10.3390/buildings12040444

Cited by 25 publications

(3 citation statements)

References 52 publications

(75 reference statements)

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“…In this study, the tansig and purelin functions were employed for making a smooth transition during training the network [107], expressed by Equations ( 7) and ( 8). This approach is also consistent with studies elsewhere [108][109][110][111][112][113][114][115][116][117].…”

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confidence: 91%

Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns

et al. 2022

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The determination of shear strength and the identification of potential failure modes are the crucial steps in designing and evaluating the structural performance of reinforced concrete (RC) columns. However, the current design codes and guidelines do not clearly provide a detailed procedure for governing failure types of RC columns. This study predicted the shear strength and identified the failure modes of rectangular RC columns using various Machine Learning (ML) models. Six ML models, including Multivariate Adaptive Regression Splines (MARSs), Naïve Bayes (NBs), K-nearest Neighbors (KNNs), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to calculate the shear strength and to classify the failure modes of rectangular RC columns. A total of 541 experimental data samples were collected from literature and utilized for developing the ML models. The results reveal that the ANN and KNNs models outperformed other ML models in predicting the shear strength of rectangular RC columns with the value larger than 0.99. Additionally, the KNNs model achieved the highest accuracy, mostly 100%, for identifying the failure modes of rectangular RC columns. Based on the superior performance of the ANN and KNNs models, a graphical user interface was also developed to rapidly predict the shear strength and failure modes of rectangular RC columns.

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confidence: 91%

Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns

et al. 2022

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“…Due to their outstanding ability to identify nonlinear and ambiguous relationships between the input and output variables of a dataset, machine learning (ML) approaches have seen significant growth in terms of applications for solving real-world problems. [13][14][15][16][17][18][19][20][21] Various powerful ML models including tree-based ensembles, support vector machines (SVM), artificial neural networks (ANNs), multivariate adaptive regression splines (MARS), and so forth. are very effectively been used for modeling various complex engineering properties of composite materials.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Kumar

Rai

Samui

2023

Structural Concrete

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Concrete which is the most commercialized construction material and thus it plays a key role in this era of development and hence its evolution is of utmost importance and therefore the evolution of the quality of concrete to that of its highly evolved type namely, ultra‐high performance concrete (UHPC) is undeniably the boon to this sector. Though, the correlations between the technical characteristics of UHPC and the composition of its mixture are complicated, nonlinear, and complex to characterize using standard statistical techniques. This paper is intended to use both deep neural network and ensemble machine learning algorithms namely gradient boosting, extreme gradient boosting, random forest regressor, extra tree regressor, and voting regressor trained with an 810 UHPC mixture collections with 15 input variables to predict its compressive strength. After adjusting a regression model, the prediction efficiency and generalization ability of the developed models are validated using a number of performance parameters. It was established that all employed models performed better at forecasting result, the extra tree regressor was the most accurate followed by extreme gradient boosting.

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“…In particular, the large-scale urban underground traffic construction has brought many new challenges to construction safety and operation safety, and has become a scientific problem that needs to be solved urgently in the construction of underground projects. Some scholars pointed out that the safety assessment of tunnel stability is crucial to tunnel construction, and accurate analysis can lead to reliable predictions [1][2][3][4]. Under the guidance of the NATM theory, China has formed a variety of tunnel support concepts and technologies.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study and Engineering Application of the Spatial Reticulated Grid Bolt-Shotcrete Support Structure for Excavation Tunnels

et al. 2022

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In view of the problems of rapid construction and deformation control of large cross-section tunnels in complex urban environments, the concept of “timely high-strength support” was put forward to form strong support over time and bear the load of surrounding rock in real time, and innovatively developed the spatial reticulated grid bolt-shotcrete support structure (SRGB). The research in this paper focuses on the supporting principle, bearing mechanical properties, snatch simulation test, construction process, and engineering application effects. The components of the SRGB and their respective functions were expounded in detail. The failure modes and deformation characteristics of the tested components were revealed through the indoor loading test, and the performance characteristics of the spatial reticulated grid concrete components with high strength and high rigidity were displayed. With the help of the multi-functional operation trolley, the assembly process test of the spatial reticulated grid was carried out, which verified the feasibility of mechanized construction and the rationality of structural design. Relying on the underground tunnel project of Guangzhou Metro, the spatial reticulated grid structure was successfully used as the initial support of the tunnel for the first time, forming a complete set of mechanized rapid construction technology for a large cross-section tunnel, and constructing a timely high-strength support system suitable for the complex urban environment. The research results provide a new idea and new technology for the mechanized rapid construction and efficient support of large cross-section tunnels.

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Neural Network-Based Prediction Model for the Stability of Unlined Elliptical Tunnels in Cohesive-Frictional Soils

Cited by 25 publications

References 52 publications

Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns

Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Experimental Study and Engineering Application of the Spatial Reticulated Grid Bolt-Shotcrete Support Structure for Excavation Tunnels

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