Settlement Prediction of Foundation Pit Excavation Based on the GWO‐ELM Model considering Different States of Influence

Qiao, Shuqing; Tan, Junkun; Zhang, Yonggang; Li-jun, Wan; Zhang, Mingfei; Tang, Jun; He, Qing

doi:10.1155/2021/8896210

Cited by 14 publications

(10 citation statements)

References 32 publications

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“…This drawback can be resolved by integrating the ELM/KELM with an optimisation approach to achieve the optimal input weights and hidden layer biases that guarantee the best ELM/KELM performance ( 27 ). Therefore, one of the most popular improvements of the ELM is the Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM), where the GWO is integrated into ELM in order to obtain the best input weights and biases ( 14 ). GWO was established by studying the hunting behavior of gray wolves ( 28 ).…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the Principal Component Analysis (PCA) is considered one of the most recognized dimensionality reduction techniques ( 13 ), where it condenses most of the information in the database into a small dimensions' number. In addition, recently, the Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) has been considered one of the most popular ML algorithms ( 14 ). Therefore, the major aims of this study are as follows:…”

Section: Introductionmentioning

confidence: 99%

“…(11)(12)(13)(14)(15)(16)(17) (44-46) depict these evaluation measurements. accuracy = TP + TN TP + TN + FN + FP (…”

mentioning

confidence: 99%

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Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Albadr

Ayob

Tiun

et al. 2022

Front. Public Health

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Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Albadr

Ayob

Tiun

et al. 2022

Front. Public Health

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“…It was found that the use of particle swarm optimization was more effective and the PSO-ANFIS model was used to predict the ground settlement of the shield tunnel with good results. Qiao et al 15 proposed a combination prediction model called a grey wolf optimization algorithm and extreme learning machine (GWO-ELM) to predict ground subsidence caused by excavation pits in different influencing states. They found that the predicted results were good.…”

Section: Introductionmentioning

confidence: 99%

A novel combined intelligent algorithm prediction model for the tunnel surface settlement

Wang,

Dai,

Jia

et al. 2023

Sci Rep

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To ensure the safety and stability of the shield tunnel construction process, the ground settlement induced by the shield construction needs to be effectively predicted. In this paper, a prediction method combining empirical mode decomposition (EMD), chaotic adaptive sparrow search algorithm (CASSA), and extreme learning machine (ELM) is proposed. First, the EMD is used to decompose the settlement sequence into trend vectors and fluctuation vectors to fully extract the effective information of the sequence; Second, the sparrow search algorithm is improved by introducing Cubic chaotic mapping to initialize the population and adaptive factor to optimize the searcher’s position formula, and the chaotic adaptive sparrow search algorithm is proposed; Finally, the CASSA-ELM prediction model is constructed by using CASSA to find the optimal values of weights and thresholds in the extreme learning machine. The fluctuation components and trend components decomposed by EMD are predicted one by one, and the prediction results are superimposed and reconstructed to obtain the predicted final settlement. Taking a shield interval in Jiangsu, China as an example, the meta-heuristic algorithm-optimized ELM model improves the prediction accuracy by 10.70% compared with the traditional ELM model. The combined EMD-CASSA-ELM prediction model can greatly improve the accuracy and speed of surface settlement prediction, and provide a new means for safety monitoring in shield tunnel construction. Intelligent prediction methods can predict surface subsidence more automatically and quickly, becoming a new development trend.

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“…Shi-fan et al proposed a GWO-ELM model to enable training and prediction of ground subsidence. e optimized GWO-ELM model has significantly improved prediction ability and better prediction effect [5]. Zhan et al proposed an Elman network-based surface settlement prediction method to predict the surface settlement of deep foundation pits in oceanic lots and then correct the predicted values by the Markov chain model, thus further improving the accurate prediction of deep foundation pits in deep marine areas.…”

Section: Introductionmentioning

confidence: 99%

Research on the Settlement Prediction Model of Foundation Pit Based on the Improved PSO-SVM Model

Song

Liu

Jiang

et al. 2022

Scientific Programming

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This paper presents a settlement prediction method based on PSO optimized SVM for improving the accuracy of foundation pit settlement prediction. Firstly, the method uses the SA algorithm to improve the traditional PSO algorithm, and thus, the overall optimization-seeking ability of the PSO algorithm is improved. Secondly, the improved PSO algorithm is used to train the SVM algorithm. Finally, the optimal SVM model is obtained, and the trained model is used in foundation pit settlement prediction. The results suggest that the settling results obtained from the optimized model are closer to the actual values and also more advantageous in indicators such as RMSE. The fitting value R2 = 0.9641, which is greater, indicates a better fitting effect. Thus, it is indicated that the improvement method is feasible.

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Settlement Prediction of Foundation Pit Excavation Based on the GWO‐ELM Model considering Different States of Influence

Cited by 14 publications

References 32 publications

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

A novel combined intelligent algorithm prediction model for the tunnel surface settlement

Research on the Settlement Prediction Model of Foundation Pit Based on the Improved PSO-SVM Model

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