A study of soft computing models for prediction of longitudinal wave velocity

Singh, Jayraj; Verma, A. K.; Banka, Haider; Singh, T. N.; Maheshwar, Sachin

doi:10.1007/s12517-015-2115-x

Cited by 24 publications

(8 citation statements)

References 15 publications

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“…To accurately predict landslide displacement, Zhu et al [16] proposed an LSSVM model optimized by the GA. However, the GA requires the preset of several calculation parameters, which brings a lot of trouble to the predictive work [22].…”

Section: Related Workmentioning

confidence: 99%

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

Zhang

Wang

et al. 2022

Applied Sciences

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Subway station projects are characterized by complex construction technology, complex site conditions, and being easily influenced by the surrounding environment; thus, construction safety accidents occur frequently. In order to improve the computing performance of the early risk warning system in subway station construction, a novel model based on least-squares support vector machines (LSSVM) optimized by quantum-behaved particle swarm optimization (QPSO) was proposed. First, early warning factors from five aspects (man, machine, management, material, and the environment) were selected based on accident causation theory and literature research. The data acquisition method of each risk factor was provided in detail. Then, the LSSVM with strong small sample analysis and nonlinear analysis abilities was chosen to give the early warning. To further ameliorate the early warning accuracy of the LSSVM, QPSO with a strong global retrieval ability was used to find the optimal calculation parameters of the LSSVM. Seventeen subway stations of Chengdu Metro Line 11 in China were picked as the empirical objects. The results demonstrated that the best regularization parameter was 1.742, and the best width parameter was 14.167. The number of misjudged samples of the proposed model was 1, and the early warning error rate was only 4.41%, which met the needs of engineering practice. Compared with the classic and latest methods, the proposed model was found to have a faster prediction speed and higher prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

Zhang

Wang

et al. 2022

Applied Sciences

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“…SVMs are increasingly used to solve nonlinear regression issues in multi-dimension estimation. It translates data from one dimension to another and then organizes them by an ideal hyper plane with greatest margin [12]. It identifies a non-linear decision boundary in the input space.…”

Section: Support Vector Regression (Svr) For Grade Estimationmentioning

confidence: 99%

“…Support vector regression (SVR) with structural risk minimization (SRM) aims to reduce generalized error and improve performance. This regularization term impacts the hypothesis space's complexity [12]. The primary benefit of SVR is to convert the nonlinear regression problems of input space into linear regression in high dimensional feature space [13,14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of machine learning algorithms for grade estimation using GRNN & SVR

2022

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Estimates of the ore grade need precise location prediction using the limited drill data available. It is a significant and important phase in the process of deciding how to invest in and develop various mining operations. This study analyses the effectiveness of using General Regression Neural Networks (GRNN) and Support Vector Regression (SVR) to improve grade estimate prediction in western India. The algorithm used to estimate grade took into account supplemental lithological information that differed throughout the study area. In this particular investigation, the factors considered as input variables were three-dimensional geographical coordinates and four underlying lithological units. On the other hand, the factors considered as output variables were CaO, Al2O3, Fe2O3, and SiO2. Ordinary kriging (OK), a geo-statistical technique, was utilized in order to verify the results of the comparative analysis performed on these models (OK). The GRNN model performed significantly better than the SVR model and the OK model when it came to generalization and the ability to predict grades.

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“…Hasanipanah et al (2015), Dindarloo (2015), and Masmoudi et al (2020) practiced support vector machines and machine learning methodology to investigate environmental issues. In addition, genetic algorithms have been widely used to predict blast-induced ground vibrations (e.g., Faradonbeh et al 2016;Singh et al 2016;Azimi et al 2019;Tian et al 2019). New prediction methods have provided some new perspectives on ground vibration forecasting.…”

Section: Introductionmentioning

confidence: 99%

Prediction and evaluation of blast-induced ground vibrations for structural damage and human response

Hudaverdi

Akyildiz

2021

Arab J Geosci

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This research focuses on blast-induced structural damage and human response to vibrations. Frequency is one of the important parameters that affect damage probability and human perception. The first and primary target of the study is to predict ground vibration and frequency simultaneously. A feed-forward back propagation neural network was created for modeling. Three operational parameters and vibration measurement distance were used as input parameters. The developed model was compared to the regression equations. Different error measures were considered to perform a vigorous model validation. Ten different blasting level charts were used to estimate structural damage probability. The regulations from North America, Europe, and Southern Hemisphere were examined and compared. Variation in the vibration regulations was also discussed. Some suggestions were put forward for the development of an international regulation. Two practical charts were created to predict human response to vibrations. A specific graph, which was proposed for pile driving induced vibrations, was considered for evaluation of blast vibrations. The developed soft computing framework can forecast probability of structural damage and human perception of vibrations effectively.

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A study of soft computing models for prediction of longitudinal wave velocity

Cited by 24 publications

References 15 publications

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

Evaluation of machine learning algorithms for grade estimation using GRNN & SVR

Prediction and evaluation of blast-induced ground vibrations for structural damage and human response

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