Evaluation of the earthquake induced uplift displacement of tunnels using multivariate adaptive regression splines

Zheng, Gang; Yang, Peipei; Zhou, Haizuo; Zeng, Chao-Feng; Yang, Xinyu; He, Xinping; Yu, Xiaozhou

doi:10.1016/j.compgeo.2019.103099

Cited by 54 publications

(9 citation statements)

References 49 publications

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“…On the other side, Ferreira et al (2019) applied MARS approach to the daily reference evapotranspiration modeling with limited weather data. Moreover, Zheng et al (2019) tried to analyze earthquake by using MARS methodology. Furthermore, Zhang et al (2019) made an assessment related to the pile drivability.…”

Section: Literature On Methodologymentioning

confidence: 99%

Türkiye'de Yenilenebilir Enerji Tüketimini Etkileyen Faktörlerinin MARS Metodolojisi İle Belirlenmesi

Yüksel¹,

Ubay²

2020

Ekonomi İşletme Ve Maliye Araştırmaları Dergisi

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The aim of this study is to determine the factors affecting the renewable energy consumption in Turkey. In this context, firstly, similar studies in the literature have been examined. As a result of the investigation, 11 different variables have been identified that may affect the use of renewable energy. Annual data of the mentioned variables in the period of 1990-2018 are taken into consideration. On the other hand, MARS method is used in the analysis process of the study. As a result, it has been determined that renewable energy use increases when the population in the country goes up. As can be seen from here, with the increasing population, the demand for energy has also increased. As a result, renewable energy has started to be used more. In addition, it is also determined that the increase in natural gas prices leads to higher consumption of renewable energy. In the event that natural gas becomes more expensive, it is understood that people are turning to other alternatives. The loan amount in the country is another factor that has an impact on renewable energy consumption. In case the loan amount exceeds a certain rate, it is seen that these loans are concentrated on non-renewable energy sources. In addition, it has been determined that there is a negative relationship between carbon emissions in the country and renewable energy use. It can be understood that renewable energy usage can be increased mainly because of the obligatory reasons, such as higher demand for energy and natural gas prices increase. This indicates that no sufficient consciousness is formed in Turkey for renewable energy. Therefore, it is important to provide the necessary incentives such as tax advantage by the state to make renewable energy use more attractive.

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Section: Literature On Methodologymentioning

confidence: 99%

Türkiye'de Yenilenebilir Enerji Tüketimini Etkileyen Faktörlerinin MARS Metodolojisi İle Belirlenmesi

Yüksel¹,

Ubay²

2020

Ekonomi İşletme Ve Maliye Araştırmaları Dergisi

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“…Another study describes the development of a simple approach to predicting the displacement of underground structures caused by earthquakes. The method used is the MARS model approach, to predict the lift displacement of underground structures and evaluate the buoyancy of underground structures in terms of earthquake parameters, structural characteristics, and soil properties [11]. Similar research on ground motion prediction, explains that ground motion prediction equations (GMPEs) are empirical relationships used to determine the response of the ground peak at a certain distance from the earthquake source.…”

Section: Introductionmentioning

confidence: 99%

Analysis of earthquake hazards prediction with multivariate adaptive regression splines

Priyanto

Zarlis

Mawengkang

et al. 2022

IJECE

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<p><span>Earthquake research has not yielded promising results, either in the form of causes or revealing the timing of their future events. Many methods have been developed, one of which is related to data mining, such as the use of hybrid neural networks, support vector regressor, fuzzy modeling, clustering, and others. Earthquake research has uncertain parameters and to obtain optimal results an appropriate method is needed. In general, several predictive data mining methods are grouped into two categories, namely parametric and non-parametric. This study uses a non-parametric method with multivariate adaptive regression spline (MARS) and conic multivariate adaptive regression spline (CMARS) as the backward stage of the MARS algorithm. The results of this study after parameter testing and analysis obtained a mathematical model with 16 basis functions (BF) and 12 basis functions contributing to the model and 4 basis functions not contributing to the model. Based on the level of variable contribution, it can be written that the epicenter distance is 100 percent, the magnitude is 31.1 percent, the location temperature is 5.5 percent, and the depth is 3.5 percent. It can be concluded that the results of the prediction analysis of areas in Lombok with the highest earthquake hazard level are Malaka, Genggelang, Pemenang, Tanjung, Tegal Maja, Senggigi, Mangsit. Meninting, and Malimbu.</span></p>

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“…Moreover, the WOA optimizer has been used to satisfactorily calculate the optimal MARS hyperparameters. In addition, previous research has indicated that MARS is a very effective tool for use in a large number of real applications, including soil erosion susceptibility prediction [36], rapid chloride permeability prediction of self-compacting concrete [37], evaluation of the earthquake induced uplift displacement of tunnels [38], estimation of hourly global solar radiation [39], atypical algal proliferation modeling in a reservoir [40], pressure drop estimation produced by different filtering media in microirrigation sand filters [41], assessing frost heave susceptibility of gravelly soils [42] and so on. However, it has never been used for evaluating superconducting critical temperature T c from the input physico-chemical parameters in most types of superconductors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

Nieto

García–Gonzalo

Paredes–Sánchez

2021

Neural Comput & Applic

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This study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.

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Evaluation of the earthquake induced uplift displacement of tunnels using multivariate adaptive regression splines

Cited by 54 publications

References 49 publications

Türkiye'de Yenilenebilir Enerji Tüketimini Etkileyen Faktörlerinin MARS Metodolojisi İle Belirlenmesi

Türkiye'de Yenilenebilir Enerji Tüketimini Etkileyen Faktörlerinin MARS Metodolojisi İle Belirlenmesi

Analysis of earthquake hazards prediction with multivariate adaptive regression splines

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

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