Formulation of multi-hazard damage prediction (MhDP) model for tunnelling projects in earthquake and landslide-prone regions: A novel approach with artificial neural networking (ANN)

Ansari, Abdullah; Rao, K S; Jain, A K; Ansari, Anas

doi:10.1007/s12040-023-02178-y

Cited by 11 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, for the horizontal stress of the structure roof, the values of RMSE and MAE are large. The reason is that the computing values of RMSE and MAE are related with the magnitude of the data [27,35]. Because the magnitude of the data for the horizontal stress is much larger than that for the vertical displacement, there are big differences for the computing values of RMSE and MAE between them.…”

Section: Analysis Of Predication Resultsmentioning

confidence: 99%

“…Because the magnitude of the data for the horizontal stress is much larger than that for the vertical displacement, there are big differences for the computing values of RMSE and MAE between them. Moreover, the computing value of R is not related with the magnitude of the data [27,35]. Therefore, its result is relatively effective.…”

Section: Analysis Of Predication Resultsmentioning

confidence: 99%

“…By using the trial-and-error method, the determined hyperparameters of the DBN model are summarized in Table 5. As one typical artificial intelligence method, the ANN is generally used for prediction study [27,[35][36][37]. Therefore, it is used here for comparison.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, based on the selected information, the risk assessment of utility tunnels can be conducted using different methods, such as an integrated model based on dynamic hazard scenario identification, Bayesian networks and risk analysis [25] and risk interaction-based deep learning [26]. Lastly, based on the collected data from previous studies, by using the traditional artificial neural network (ANN), the damage of a tunnel deeply buried in a mountain affected by an earthquake and landslide has been predicted [27]. It can be found that, in those studies, although the safety operation of a utility tunnel or a tunnel affected by some factors using monitored information has been analyzed, the used information is collected from other studies.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning

Gao,

Ge,

Gao

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The underground utility tunnel in a soft foundation is generally affected by the serious disturbance of the vehicle load during the operation period. Therefore, in this study, for the typical utility tunnel engineering in Suqian City of Jiangsu Province, China, field tests were conducted to monitor the performance of the utility tunnel structure in a soft foundation affected by the ground traffic loads during the operation period. Based on the test results, the datasets whose number is 15,376, composed of the five main disturbance factors (four vehicle operating load parameters and one operating time parameter), and the corresponding two main structure responses (displacement and stress) have been constructed. Based on the obtained datasets, using the proposed new deep learning model called WO-DBN, in which the seven hyperparameters of a deep belief network (DBN) are determined by the whale optimization algorithm (WOA), the safety responses of the utility tunnel structure have been predicted. The results show that for the prediction results, the average absolute error for the displacement is 0.1604, and for the stress, it is 12.3726, which are not significant and can meet the requirement of the real engineering. Therefore, the deep learning model can accurately predict the performance of the utility tunnel structure under a vehicle load and other disturbances, and the model has good applicability.

show abstract

Section: Analysis Of Predication Resultsmentioning

confidence: 99%

Section: Analysis Of Predication Resultsmentioning

confidence: 99%