Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement

Li, Xiao; Liu, Xin; Li, Clyde Zhengdao; Hu, Zhumin; Shen, Geoffrey Qiping; Huang, Zhenyu

doi:10.1177/1475921718767935

Cited by 38 publications

(20 citation statements)

References 39 publications

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“…The subsequent monitoring is performed once every one month thereafter. With the Code for Design of Building Foundation as the standard for controlling the deformation of structures, the relevant allowable values for the deformation of different structures during the construction are formulated [ 7 ]. The measuring instruments include DINI03 surveyor’s level (Trimble Navigation, USA) and GTS102N total station (Beijing Topcon, China).…”

Section: Methodsmentioning

confidence: 99%

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

2020

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The objective is to improve the prediction accuracy of foundation pit deformation in geotechnical engineering, thereby provide early warning for engineering practice. The digital closerange photogrammetry is used to obtain monitoring data. The error compensation method is used to optimize the center of the monitoring point. Aiming at the limitations of back propagation neural network (BPNN), a genetic algorithm (GA)-optimized BPNN algorithm is proposed. Then, the optimized algorithm is applied to predict the deformation and displacement of foundation pits from three aspects, i.e., simple horizontal displacement, simple longitudinal displacement, and the combination of horizontal and longitudinal displacements. Meanwhile, the time domain, space domain, and time-space domain are used as input features to compare the prediction results of the BPNN model and the GA-optimized BPNN model. Finally, the GA-improved BPNN is compared with the Support Vector Regression (SVR) model and Random Forest (RF) model. The results show that the prediction result, obtained by simultaneously using horizontal displacement and longitudinal displacement as input features, has smaller errors; also, the actual output is closer to the expected output. Compared with the prediction result with time domain and space domain as input features, the prediction result with time-space domain as input features is closer to the expected output. Taking the combination of time and space domains as input features, compared with the BPNN model, the GA-optimized BPNN model has a lower Root Mean Squared Error (RMSE) value (0.0163), a larger Index of Agreement (IA) value (0.9800), and a shorter training time (7.08 s). Compared with the SVR model and RF model, the GA-improved BPNN model has a lower Root Mean Squared Error (RMSE) value (0.0211), a larger Index of Agreement (IA) value (0.9706), and shorter training time (7.61 s). Therefore, the foundation pit deformation prediction model based on BPNN and GA has strong prediction ability, which can be popularized and applied in similar geotechnical engineering.

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Section: Methodsmentioning

confidence: 99%

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

2020

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“…In recent years, artificial intelligence technology has been developed rapidly, but at present, artificial intelligence technology still has some shortcomings and limitations [16][17][18]. Firstly, it needs more data and data support to build the model.…”

Section: Need For Researchmentioning

confidence: 99%

“…To predict the settlement of rock-socketed piles, Danial et al [22,23] presented the development of a hybrid ANN-based model named PSO-ANN (or neuro-swarm) with detailed modeling process and a new model based on gene expression programming (GEP). Li et al [16] use least squares support vector machines (LSSVMs) based on multipoint measurement to monitor and predict foundation pit displacement. In addition, fuzzy comprehensive evaluation (FCE) method is also a typical artificial intelligence technology; it can better solve the fuzzy but difficult to quantify the problem and is suitable for all kinds of nondeterministic problems.…”

Section: Need For Researchmentioning

confidence: 99%

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A Prediction Method for Postconstruction Settlement of Pile‐Soil Composite Subgrade Based on Fuzzy Comprehensive Evaluation

Shan

Jiang

Chang

et al. 2021

Advances in Civil Engineering

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This paper presents a postconstruction settlement prediction method for pile-soil composite subgrade based on the multilevel fuzzy comprehensive evaluation principle. In this method, the variation range of postconstruction settlement can be obtained from a simple calculation based on the basic data of actual engineering. Firstly, according to the characteristics of influencing factors in the construction of soft soil subgrade, the evaluation index set and two-level factor index sets were selected. The grading standards of the evaluation index and factor index were determined according to the allowable value of the standard and the numerical simulation results. Secondly, each factor index was standardized, and the normal distribution function in the form of exponential was used to construct the standard membership function for the first and second factor indexes. Finally, the comprehensive evaluation matrix of postconstruction settlement of composite subgrade was constructed based on the entropy weight method. The variation range of postconstruction settlement was predicted by the principle of maximum membership. The example analysis shows that the predicted results of the prediction method and the field measurement method are in good agreement, indicating that the proposed method can realize the postconstruction settlement prediction of composite subgrade, and the results are more accurate and more instructive.

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“…[4][5][6] Meanwhile, foundation pit displacement in the excavation is a critical safety risk for both building structure and people's lives. 7 Thus, building monitoring system of deep excavation to ensure the structural safety and stability of the surrounding buildings and environment is becoming increasingly important. A large number of researches, involving in design, construction and monitoring of foundation pit engineering, have been carried out in China and overseas.…”

Section: Introductionmentioning

confidence: 99%

An assessment model of monitoring risk in deep excavation based on fuzzy theory

Long

2019

Indoor and Built Environment

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In order to better evaluate the monitoring risk of deep excavation, an assessment model based on fuzzy theory was established by combining the uncertainty and membership degree of evaluation indexes with the fuzziness of experts’ comments. Firstly, considering the risk sources as evaluation indexes, the monitoring risk evaluation system for deep excavation was establish, and the risk evaluation function was constructed. Secondly, the risk assessment model based on fuzzy theory was put forward, analytic hierarchy process was used to determine the weight of evaluation indexes, and expert evaluation weight was introduced to correct the evaluation function. Finally, comprehensive risk grade in deep excavation was obtained and the risk decisions were put forward by calculating the value of modified evaluation function. Through the application of the monitoring risk assessment model in the deep excavation of World Trade Group, the results are in accordance with the engineering practice. The results suggest that the model based on fuzzy theory is efficient for evaluating the monitoring risks and can be used in the application of deep excavation evaluation. Meanwhile, the applicability and accuracy of the assessment model are verified.

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Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement

Cited by 38 publications

References 39 publications

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

A Prediction Method for Postconstruction Settlement of Pile‐Soil Composite Subgrade Based on Fuzzy Comprehensive Evaluation

An assessment model of monitoring risk in deep excavation based on fuzzy theory

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