Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms

Gajan, Sivapalan

doi:10.3390/geotechnics2030038

Cited by 8 publications

(6 citation statements)

References 36 publications

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“…Both features and targets are supported in both categorical and continuous forms in tree-based algorithms. Decision Tree Regression has been employed in modelling the peak rotation and stability of shallow foundations [33].…”

Section: Decision Tree Regressionmentioning

confidence: 99%

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning

Ozturk

Kodsy²,

Iskander³

2023

Infrastructures

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Pile design is an essential component of geotechnical engineering practice, and pipe piles, in particular, are increasingly being used for the support of a variety of infrastructure projects. These piles are being used with dimensions that exceed those used in the development of the most widely used design approaches. At the same time, the growth in pile dimensions calls for the evolution of the state-of-the-art at a similar pace. The objective of this study is to provide an improved prediction of pile capacity. A database of 112 load tests on pipe piles ranging in diameter from 10 to 100 in. (0.25–2.5 m) and in length from 10 to 320 ft. (3–98 m) was employed in this study. First, design capacities were computed using four popular design methods and compared to capacities interpreted from a load test. For the employed dataset, the Revised Lambda method was found to best predict capacities of pipe piles obtained from a load test, among the four examined methods, and was thus employed as a reference standard for assessing the performance of ML methods. Next, eight ML regression models were trained to compute the capacity of pipe piles. Several trained ML models predicted capacities for the testing data set on par with the Revised Lambda method, and three were selected for further investigation. A variety of pile dimensions and soil properties were examined as input properties for ML and the trained models performed surprisingly well with only the pile dimensions used as input. In addition, ML models exhibited satisfactory diameter and length effects, which have been areas of concern for some traditional design approaches. The work thus demonstrates the feasibility of employing machine learning (ML) for determining the capacity of pipe piles. A web application was also developed as a tool for forecasting the capacity of pipe piles using ML.

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Section: Decision Tree Regressionmentioning

confidence: 99%

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning

Ozturk

Kodsy²,

Iskander³

2023

Infrastructures

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“…adopted various ML methodologies, such as linear and ridge regression, decision trees, random forests, extreme gradient boosting and adaptive boosting, to propose seismic demand models of self‐centring dual rocking core systems. Similarly, Gajan 31 adopted k‐nearest neighbours, support vector machines, and random forests to predict the peak rotation and safety factor against the overturning of rocking structures under earthquakes. Recently, Achmet et al 32 .…”

Section: Introductionmentioning

confidence: 99%

A multi‐level approach to predict the seismic response of rigid rocking structures using artificial neural networks

Banimahd,

Giouvanidis,

Karimzadeh

et al. 2024

Earthq Engng Struct Dyn

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This paper explores the use of Artificial Neural Networks (ANN) for the rocking problem. The paper adopts rigid rocking blocks of different sizes and slenderness, which undergo rocking motion without sliding and bouncing when subjected to recorded earthquakes. This research focuses on the cases where the blocks overturn or safely return to their initial (rest) position at the end of the ground shaking. An ANN model is trained to efficiently categorise the response into overturning or safe rocking using the structural parameters, ground motion characteristics, and the coefficient of restitution as input. The results show the substantial contribution of velocity and frequency characteristics of the ground motion to overturning. In addition, ANN is used to predict the response amplitude and identify the most critical input variables that govern safe rocking. The analysis reveals that rocking amplitude is governed by a combination of duration, frequency, and intensity characteristics of the ground excitation. Interestingly, the maximum incremental velocity (MIV), a novel intensity measure for the rocking literature, shows a substantial correlation with the rocking amplitude. In this context, this paper proposes closed‐form expressions using the most influential input variables to provide a quick, yet adequately accurate, response prediction. Finally, this study pays special attention to the contribution of the coefficient of restitution, which, in general, is less critical to the peak safe rocking response, while it becomes more important to the overturning response.

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“…Primary concerns include the potential for excessive rotation and settlement of the foundation, the risk of the structure tipping over, and the inherent challenges in precisely predicting the performance of rocking foundations, especially in the face of uncertainties in soil properties and earthquake loading. These concerns collectively hinder the widespread adoption of foundation rocking as a design approach for mitigating seismic forces and ductility demands imposed on structures [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Effect of Footing Shape on the Rocking Behavior of Shallow Foundations

Khezri,

Moradi,

Mir Mohammad Hosseini

et al. 2024

Buildings

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Sources such as wind or severe seismic activity often exert extreme lateral loading onto the shallow foundations supporting high-rise structures such as bridge piers, buildings, shear walls, and wind turbine towers. Such loading conditions may cause the foundation to exhibit nonlinear responses such as uplift and bearing capacity mobilization of the supporting soil (i.e., rocking behavior). Previous numerical and experimental studies suggest that while such inelastic behaviors may engender residual deformations in the soil–foundation system, they offer potential benefits to the overall integrity of structures through dissipating energy and reducing inertia forces transmitted to the superstructure, thereby limiting seismic demand on structural elements. This study investigates the effect of footing shape on the rocking performance of shallow foundations in different subgrade densities and initial vertical factor of safety (FSv). To this end, a series of reduced-scale slow cyclic tests under 1 g condition were conducted using a single degree of freedom (SDOF) structure model. The performance of different footing shapes was studied in terms of moment capacity, recentering ratio, rocking stiffness, damping ratio, and settlement. For three foundations with different length-to-width ratios, the results indicate that increasing the safety factor and length-to-width ratio leads to thinner, S-shaped moment–rotation curves, mainly owing to the enhanced recentering capability and the P-δ effect. Moreover, across all foundation types, the repetition of a limited loading cycles with consistent rotation amplitude does not cause stiffness degradation or moment capacity reduction.

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Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms

Cited by 8 publications

References 36 publications

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning

A multi‐level approach to predict the seismic response of rigid rocking structures using artificial neural networks

Effect of Footing Shape on the Rocking Behavior of Shallow Foundations

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