• Geomechanics and Engineering
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• 제16권6호
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• pp.655-667
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• 2018

To evaluate the stability of a rock slope with one pre-exiting vertical crack, this paper performs corresponding probabilistic stability analysis. The existence of cracks is generally ignored in traditional deterministic stability analysis. However, they are widely found in either cohesive soil or rock slopes. The influence of one pre-exiting vertical crack on a rock slope is considered in this study. The safety factor, which is usually adopted to quantity the stability of slopes, is derived through the deterministic computation based on the strength reduction technique. The generalized Hoek-Brown (HB) failure criterion is adopted to characterize the failure of rock masses. Considering high nonlinearity of the limit state function as using nonlinear HB criterion, the multivariate adaptive regression splines (MARS) is used to accurately approximate the implicit limit state function of a rock slope. Then the MARS is integrated with Monte Carlo simulation to implement reliability analysis, and the influences of distribution types, level of uncertainty, and constants on the probability density functions and failure probability are discussed. It is found that distribution types of random variables have little influence on reliability results. The reliability results are affected by a combination of the uncertainty level and the constants. Finally, a reliability-based design figure is provided to evaluate the safety factor of a slope required for a target failure probability.

• Geomechanics and Engineering
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• 제16권6호
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• pp.577-588
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• 2018

A major concern in deep excavation project in soft clay deposits is the potential for adjacent buildings to be damaged as a result of the associated excessive ground movements. In order to accurately determine the wall deflections using a numerical procedure such as the finite element method, it is critical to use the correct soil parameters such as the stiffness/strength properties. This can be carried out by performing an inverse analysis using the measured wall deflections. This paper firstly presents the results of extensive plane strain finite element analyses of braced diaphragm walls to examine the influence of various parameters such as the excavation geometry, soil properties and wall stiffness on the wall deflections. Based on these results, a multivariate adaptive regression splines (MARS) model was developed for inverse parameter identification of the soil relative stiffness ratio. A second MARS model was also developed for inverse parameter estimation of the wall system stiffness, to enable designers to determine the appropriate wall size during the preliminary design phase. Soil relative stiffness ratios and system stiffness values derived via these two different MARS models were found to compare favourably with a number of field and published records.

• Computers and Concrete
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• 제24권2호
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• pp.137-150
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• 2019

In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.

• 대한지리학회지
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• 제51권2호
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• pp.219-233
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• 2016

수리 또는 계량적 모형을 사용하는 사회과학연구에서 분석의 초점은 종속변수와 설명변수의 관계를 밝히는 것, 즉 설명 중심의 모형(explanatory modeling)이 지금까지 주류를 이루었다. 반면 예측(prediction) 능력 제고에 초점을 맞춘 분석은 드물었다. 본 연구에서는 이론 및 가설을 검증하거나 변수 간의 관계를 밝히는 설명 중심의 모형이 아니라 신규 관찰치에 대한 예측 오차를 줄이는, 예측 중심의 비모수 모형(non-parametric model)을 검토하였다. 서울시 강남구를 사례지역으로 선정한 후, 2011년부터 2014년까지 신고된 단독주택 실거래가를 기초자료로 하여 주택가격을 추정하였다. 적용한 비모수 모형은 기계학습 분야에서 제시된 일반가산모형(generalized additive model), 랜덤 포리스트, MARS(multivariate adaptive regression splines), SVM(support vector machines) 등이며 비교적 최근에 개발된 MARS나 SVM의 예측력이 뛰어남을 확인할 수 있었다. 마지막으로 이러한 비모수 모형에 공간적 자기상관성을 추가적으로 반영한 결과, 모형의 가격 예측력이 보다 개선되었음을 알 수 있었다. 본 연구를 계기로 그간 모수 모형에 집중되었던 부동산 가격추정 방법론이 비모수 모형으로 확대 및 다양화되기를 기대한다.

• Computers and Concrete
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• 제25권1호
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• pp.1-14
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• 2020

This study presents applications of the multivariate adaptive regression splines (MARS) method for predicting the ultimate loading carrying capacity (N_u) of rectangular concrete-filled steel tubular (CFST) columns subjected to eccentric loading. A database containing 141 experimental data was collected from available literature to develop the MARS model with a total of seven variables that covered various geometrical and material properties including the width of rectangular steel tube (B), the depth of rectangular steel tube (H), the wall thickness of steel tube (t), the length of column (L), cylinder compressive strength of concrete (f'_c), yield strength of steel (f_y), and the load eccentricity (e). The proposed model is a combination of the MARS algorithm and the grid search cross-validation technique (abbreviated here as GS-MARS) in order to determine MARS' parameters. A new explicit formulation was derived from MARS for the mentioned input variables. The GS-MARS estimation accuracy was compared with four available mathematical methods presented in the current design codes, including AISC, ACI-318, AS, and Eurocode 4. The results in terms of criteria indices indicated that the MARS model was much better than the available formulae.

• Journal of Information Processing Systems
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• 제8권4호
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Smart Structures and Systems
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• 제32권1호
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Structural Engineering and Mechanics
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• 제60권3호
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• pp.471-488
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• 2016

This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled with high energy-dissipation capability. To investigate the behavior of this damper in structures, a steel frame is modeled with this device subjected to monotonic and cyclic loading. Several response parameters are considered, and the performance of damper in reducing each response is evaluated. MLR and MARS methods were used to predict the strength of this damper. Displacement was determined to be the most effective parameter of damper strength, whereas the thickness did not exhibit any effect. Adding thickness parameter as inputs to MARS and MLR models did not increase the accuracies of the models in predicting the strength of this damper. The MARS model with a root mean square error (RMSE) of 0.127 and mean absolute error (MAE) of 0.090 performed better than the MLR model with an RMSE of 0.221 and MAE of 0.181.

• Geomechanics and Engineering
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• 제14권3호
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• pp.257-269
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• 2018

Analysis of slope stability failures, as one of the complex natural hazards, is one of the important research issues in the field of civil engineering. Present paper adopts and investigates four soft computing-based techniques for this problem: Patient Rule-Induction Method (PRIM), M5' algorithm, Group Method of data Handling (GMDH) and Multivariate Adaptive Regression Splines (MARS). A comprehensive database consisting of 168 case histories is used to calibrate and test the developed models. Six predictive variables including slope height, slope angle, bulk density, cohesion, angle of internal friction, and pore water pressure ratio were considered to generate new models. The results of test studies are used for feasibility, effectiveness and practicality comparison of techniques with each other, and with the other available well-known methods in the literature. Results show that all methods not only are feasible but also result in better performance than previously developed soft computing based predictive models and tools. It is shown that M5' and PRIM algorithms are the most effective and practical prediction models.

• Smart Structures and Systems
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• 제29권3호
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• pp.433-444
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• 2022

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.