Numerical Study of Dig Sequence Effects during Large-Scale Excavation

Li, Danli; Chen, Ying; Dai, Bing; Wang, Zhengyi; Liang, Hao

doi:10.3390/app132011342

Cited by 5 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the investigation report and ref. [24], when the soil is the same, the physical and mechanical properties of the soil layers within the excavation range of the pit are shown in Table 1. In Table 1, Es is the compression modulus, c is the cohesion, φ is the internal friction angle, ν is the Poisson's ratio, and h is the thickness of soil layer.…”

Section: Overview Of the Project And Layout Of Measurement Pointsmentioning

confidence: 99%

Prediction of Buildings’ Settlements Induced by Deep Foundation Pit Construction Based on LSTM-RA-ANN

Hu,

2024

Applied Sciences

View full text Add to dashboard Cite

In view of the shortcomings of existing methods for predicting the settlement of surrounding buildings caused by deep foundation pit construction, this study uses the monitoring data of a foundation pit project in Shanghai and divides the construction process of the pit into three working conditions, that is, enclosure construction, earthwork excavation, and basement support construction. The attention mechanism and residual update are integrated into the artificial neural network (ANN) model, and the root-mean-square error, average absolute error, and determination coefficient are used as the evaluation indices of the model. The artificial neural network prediction model LSTM-RA-ANN for building settlements in deep foundation pit construction was then established. The prediction performance of the model was also analysed under different working conditions, and the influences of the main factors (including the soil parameter, monitoring point location, activation function, hyperparameter, and input number) on the evaluation index was further explored. The results indicate that the performances of the established LSTM-RA-ANN model are closely related to the construction conditions, the predicted settlements agree well with the monitored ones in three working conditions with the greatest errors occurring at a later time of the working conditions, and the prediction accuracy of the great–small order corresponds to basement support, enclosure construction, and earthwork excavation respectively. The farther the monitoring point is from the edge of the pit, the better the model performance is. The activation function, initial learning rate, and maximum iteration batch have a great influence on the evaluation indices of the model, while the number of input points has little effect on the evaluation indices. These results may serve as a reference for the safe construction and normal operation of foundation pit engineering.

show abstract

Section: Overview Of the Project And Layout Of Measurement Pointsmentioning

confidence: 99%

Prediction of Buildings’ Settlements Induced by Deep Foundation Pit Construction Based on LSTM-RA-ANN

Hu,

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, the majority of empirical index standards only take into account one influencing element, which has poor precision and dependability. As a result, an increasing number of academics are starting to think about using multifactorial techniques based on mathematical algorithmic models to forecast rockbursts [19,20]. Xue et al [10] optimized the ELM by particle swarm algorithm (PSO), selecting six quantitative rockburst parameters, such as the maximum tangential stress of the surrounding rock, uniaxial compressive strength of the rock, the tensile strength of the rock, the stress ratio, the brittleness ratio of the rock and the elastic energy index, on 344 sets of rockburst cases were learned and validated on the riverside hydropower station, and the results showed that the model has a better prediction effect.Yin et al [21] collected 400 groups of cases through microseismic monitoring data, optimized the CNN-Adam-BO integration algorithm by using adaptive matrix estimation and Bayesian to CNN-Adam-BO integration algorithms, respectively, and compared the model with the continuous wavelet transform and the cross-wavelet transform, which verified the superiority of the model.…”

Section: Introductionmentioning

confidence: 99%

Study on the effect of multiple optimization algorithms on rockburst prediction models

Chen,

Da,

Dai

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

The rapidly expanding area of rockburst prediction has drawn a lot of interest because of its enormous potential to lower the risk of engineering disasters, enhance mine production safety, and protect employee lives. Consequently, the goal of this research is to forecast the rockburst intensity class for the prediction objective by optimizing four single machine learning models (SVM, DT, CNN, and RF) utilizing fifteen optimization algorithms (Bayes, SSA, DBO, SCA, SA, PSO, SO, POA, GWO, IGWO, AVOA, CSA, GTO, NGO, and WSO). The hybrid models were trained using a ten-fold cross-validation, and each hybrid model's performance was examined statistically. The SMOTE method then oversampled the original dataset in order to examine how the data equalization issue affected the hybrid models. The findings demonstrate that, in the original dataset, all optimization strategies increase the accuracy of the DT, CNN, and RF models; however, the balanced original dataset has a greater impact on the SVM models. And once the dataset is balanced, every optimization algorithm improves the accuracy of the SVM model and decreases the accuracy of the DT model; however, for the CNN and RF models, the majority of optimization algorithms improve the accuracy while only a small percentage of them do the opposite. An essential reference value for the development of later rock burst prediction models is provided by this study.

show abstract

“…The construction of subway stations usually involves the excavation of a foundation pit. As subway stations are usually located in downtown areas, the construction environment may be complicated when considering the surrounding existing buildings, pipelines, main traffic roads, and subways, if any, as well as urban planning in the future [5][6][7]. Therefore, during the excavation of a foundation pit, lateral displacement of the surrounding rocks and settlement of the surrounding ground should be strictly controlled to ensure the safety of the surrounding buildings, structures, and so on.…”

Section: Introductionmentioning

confidence: 99%

Stability of a Deep Foundation Pit with Hard Surrounding Rocks under Different in-Time Transverse Supporting Conditions

Li,

Ma,

Gao

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This paper focuses on investigating the stability of a deep foundation pit with hard surrounding rocks at different excavation stages with different supporting schemes by means of numerical calculations. The supporting schemes in question were combinations of one fixed vertical support and four varied transverse supports. Drilled grouting piles were used as vertical supports, and the commonly used steel bracings and prestressed anchorages served as transverse supports. The parameters used to evaluate the stability of the foundation pit at different excavation stages included the lateral displacements of the surrounding rocks, the settlement of the surrounding ground, the axial forces of steel bracings, and displacements at the tops of the drilled grouting piles. Simulation results showed that when a transverse supporting scheme consisting of one-layer steel bracings and prestressed anchorages set at 9 m and 22.5 m underground, respectively, was adopted, the lateral displacements of the surrounding rocks and settlement of the surrounding ground at different excavation stages were the largest compared to those under the other three transverse supporting schemes, while the corresponding values were lower compared to those allowed in Chinese standard GB50007-2011, demonstrating that this kind of supporting scheme is effective in terms of ensuring the safety of the foundation pit at different excavation stages. Moreover, the setting techniques for this kind of supporting scheme were relatively simple, and the corresponding influences of supporting element arrangements on excavation techniques were the lowest. Therefore, one-layer steel bracings and one-layer prestressed anchorages constituted the most suitable transverse supporting scheme for excavating a deep foundation pit with hard surrounding rocks.

show abstract

Numerical Study of Dig Sequence Effects during Large-Scale Excavation

Cited by 5 publications

References 33 publications

Prediction of Buildings’ Settlements Induced by Deep Foundation Pit Construction Based on LSTM-RA-ANN

Prediction of Buildings’ Settlements Induced by Deep Foundation Pit Construction Based on LSTM-RA-ANN

Study on the effect of multiple optimization algorithms on rockburst prediction models

Stability of a Deep Foundation Pit with Hard Surrounding Rocks under Different in-Time Transverse Supporting Conditions

Contact Info

Product

Resources

About