No abstract

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

This paper proposes a new computational model to predict the earth pressure balance (EPB) shield performance during tunnelling. The proposed model integrates an improved particle swarm optimization (PSO) with adaptive neurofuzzy inference system (ANFIS) based on the fuzzy C-mean (FCM) clustering method. In particular, the proposed model uses shield operational parameters as inputs and computes the advance rate as the output. Prior to modeling, critical operational parameters are identified through principle component analysis (PCA). The hybrid model is applied to the prediction of the shield performance in the tunnel section of Guangzhou Metro Line 9 in China. The prediction results indicate that the improved PSO-ANFIS model shows high accuracy in predicting the EPB shield performance in terms of the multiobjective fitness function [i.e. root mean square error (RMSE) = 0.07, coefficient of determination (R 2) = 0.88, variance account (VA) = 0.84 for testing datasets, respectively]. The good agreement between the actual measurements and predicted values demonstrates that the proposed model is promising for predicting the EPB shield tunnel performance with good accuracy.

Bridge failure is one of the worst infrastructural disasters. This paper investigates the risk of bridge infrastructures in the view of sustainable management. Statistics on bridge failures from 2009 to 2019 in China show that most of these failures are related to anthropic factors. The collapse of the Zijin Bridge on 14 June 2019 in Heyuan City of Guangdong Province, China is used as a case to perform detailed analysis. Superficially, bridge collapse is a technical problem rather than a management problem. However, the deep reason for this kind of bridge failure may be due to the lack of sustainable management. In order to verify this point of view, both fault tree analysis (FTA) and strategic environmental assessment (SEA) for the bridge failure and later impact on society are conducted. According to the FTA results, the failure of the arch foot is the direct trigger of the Zijin Bridge collapse. Since a lack of real-time monitoring, risk assessment and other management issues are potential factors causing bridge collapse, strategic environmental assessment (SEA) is used to investigate the management issues related to the economy, culture, human health and environmental sustainability in more depth. The low total SEA result shows poor project management and a high safety risk. Finally, the specific managerial measures are proposed to improve the sustainability of infrastructures.