Evaluation of Group Capacity of Micropile in Soft Clayey Soil from Experimental Analysis Using SVM-Based Prediction Model

Borthakur, Nirmali; Dey, Ashim Kanti

doi:10.1061/(asce)gm.1943-5622.0001606

Cited by 19 publications

(7 citation statements)

References 38 publications

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“…After we have extracted the bearing degradation feature, we find that this feature can represent the full-cycle life of the bearing. In order to better help inspectors predict the remaining life of the bearing in advance, we adopted the support vector regression algorithm for prediction [42]. Squirrel optimization has shown good prediction ability in many fields since it was developed in 2019, so we proposed using the squirrel optimization algorithm to find the best kernel function parameters and penalty factors, and then apply them to the life prediction of bearings.…”

Section: Discussionmentioning

confidence: 99%

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Xiao

Shi

et al. 2022

Machines

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Rolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life prediction of rolling bearings. In this paper, first, the time-domain features were extracted to form the eigenvector of the vibration signal, and then the index representing the bearing degradation was found. It was found that the time-domain index could effectively describe the degradation information of the bearing, and the multi-dimensional time-domain characteristic information could effectively describe the attenuation trend of the vibration signal of the rolling bearing. On this basis, appropriate feature vectors were selected to describe the degradation characteristics of bearings. Aiming at the problems of large amounts of data, large amounts of information redundancy and unclear performance index of multi-dimensional feature vectors, the dimensionality of multi-dimensional feature vectors was reduced with principal component analysis, thus, simplifying the multi-dimensional feature vectors and reducing the information redundancy. Finally, in view of the support vector machine (SVM)’s needs to determine kernel function parameters and penalty factors, the squirrel optimization algorithm (SOA) was used to adaptively select parameters and establish the state-life evaluation model of rolling bearings. In addition, mean absolute error (MAE) and root mean squared error (RMSE) were used to comprehensively evaluate SOA. The results showed that the SOA reduced the errors by 5.1% and 13.6%, respectively, compared with a genetic algorithm (GA). Compared with particle swarm optimization (PSO), the error of SOA was reduced by 7.6% and 15.9%, respectively. It showed that SOA-SVM effectively improved the adaptability and regression performance of SVM, thus, significantly improving the prediction accuracy.

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Section: Discussionmentioning

confidence: 99%

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Xiao

Shi

et al. 2022

Machines

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“…According to the numerical simulation analysis under the overall compression mode of the composite foundation, the pile top stress sharing ratio of the mini steel pipe pile and the jet grouting pile is n p � 140 [7]. e load Q 0j shared by the top of the jet grouting pile calculated by equation ( 3) is…”

Section: E Maximum Bearing Capacity Of the Composite Pile Top Control...mentioning

confidence: 99%

“…is system can take maximum advantage of the "high strength" characteristics of mini steel pipe pile to bear the load [7] and use the reinforcement e ect of jet grouting pile on soft soil and large pile side surface area to provide su cient side friction resistance [8]. Although the load plate test is more appropriate to discuss the bearing capacity of the composite foundation [9], its deformation and load transfer law cannot be directly extended to the actual engineering design.…”

Section: Introductionmentioning

confidence: 99%

Digital Evaluation of Vertical Compressive Bearing Capacity for Jet Grouting Pile-Mini Steel Pipe Pile Composite Foundation

Wang

Mao²,

Wang

2022

Mathematical Problems in Engineering

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The jet grouting pile-mini steel pipe pile in soft soil area is composed of the jet grouting pile and the mini steel pipe pile. Through the analysis of the stress mode of the composite foundation under the overall compression of the pile top, the hypothetical diagram and calculation equation of the interfacial friction resistance distribution of the composite foundation are put forward. Thus, the calculation equation of the axial force of the mini steel pipe pile and the jet grouting pile is deduced, and the axial force distribution diagram of the mini steel pipe pile and the jet grouting pile in the composite foundation is defined. The vertical compression composite foundation in the field was selected for bearing capacity test and calculation and comparative analysis, and the relative error was 3%. Therefore, the stress mode and calculation method of the jet grouting pile-mini steel pipe pile proposed in this paper can meet the needs of engineering design and popularization and application of the composite foundation.

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“…The algorithms that are often used at present are the frame difference method, mean shift algorithm, particle filter, multifeature fusion, etc. [18,19]. These algorithms are mainly based on one of the basic algorithms such as detection, matching, filtering, etc., or the fusion of several different algorithms, so as to achieve the effect of complementary advantages [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Target Recognition, Localization, and Motion Control of Soccer Robot

Zhu¹,

Gong²

2022

Journal of Sensors

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The realization of target tracking includes many technologies, among which the most critical and core element is the target tracking algorithm. Based on this, this paper first uses an omnidirectional vision image enhancement algorithm to identify the target and then designs an adaptive target recognition algorithm based on SVM to identify the actual target. The results show that, in the case of different illuminance, the fluctuation range of the four evaluation indexes after image enhancement is smaller than that of the original image, indicating that the brightness distribution of the image after enhancement is more uniform, which eliminates interference factors for the subsequent image recognition and tracking. The difference of recognition rate between the two algorithms is small when the illumination value is medium. When the illumination value is high or low, the recognition rate of the adaptive algorithm is much higher than that of the threshold method.

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Evaluation of Group Capacity of Micropile in Soft Clayey Soil from Experimental Analysis Using SVM-Based Prediction Model

Cited by 19 publications

References 38 publications

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Digital Evaluation of Vertical Compressive Bearing Capacity for Jet Grouting Pile-Mini Steel Pipe Pile Composite Foundation

Target Recognition, Localization, and Motion Control of Soccer Robot

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