Tunnel boring is a common construction method for urban subway tunnel excavation. However, the tunnel boring process can easily induce ground and nearby building foundation deformations. Electrical transmission towers are crucial infrastructure for urban power supply, and when tunnel boring approaches these towers, an assessment of induced tower foundation settlement should be carried out. The finite element method is a powerful tool for conducting such assessments. However, due to the complexity of the geology and construction conditions, in regions lacking engineering experience and field data, finite element predictions often exhibit significant errors and are challenging to correct. In light of these challenges, this paper proposes a correction method for the finite element prediction model of tower foundation settlement near shield tunnel excavation based on the principle of similarity between ground and foundation settlement in the same finite element model. This method first utilizes pre-measured ground settlement data to determine the expression for its finite element model factor and then introduces it as a correction term into the tower foundation settlement prediction model. The empirical constants in the correction term are updated in real-time using actual tower foundation settlement data obtained during the tunnel boring process, leading to dynamic adjustments and improved accuracy in the prediction model. Using a shield tunnel project in Foshan, Guangdong as an example, this paper validates the superiority and practical value of the proposed method. The research indicates that: Without correction, the finite element model of the tower foundation overestimated its settlement by approximately 30%, with moderate accuracy and variability in predictions. Additionally, the predicted tower inclination based on settlement data did not align with the measured values. After applying the calibration method proposed in this paper, the overall accuracy of the tower foundation finite element model became unbiased, with reduced variability. The inclination of the tower, calculated based on the calibrated settlement data, exhibited high congruence with the measured values and consistent trends.

Karst geological hazards pose a significant challenge for the urban construction and underground space development and utilization in the Guangdong-Hong Kong-Macao Greater Bay Area, especially in Guangzhou and Shenzhen. Karst exploration generally involves identifying and assessing caves through a combination of drilling and geophysical information. In recent years, the cross-hole computed tomography (CT) geophysical method has been widely used in karst exploration in the Greater Bay Area due to its ease of operation and strong ability to obtain geological information. However, the accuracy of this method in identifying caves still needs to be quantitatively evaluated. This paper collected a large amount of data on karst drilling and exploration, and the accuracy of cross-hole CT karst identification was statistically analyzed using the model factor method. The results showed that this method could accurately detect the buried depth of the cave roof, floor and height, with an average error less than 5%. The predictive accuracy of the buried depths of the cave roof and floor has very low variability, only 5%, while the predictive accuracy of the cave height has medium variability, exceeding 35%. The accuracy stability of the cross-hole CT karst identification method is satisfactory and not affected by the factors such as CT method type, cave filling condition, emission and reception point distance, drilling type, cave roof thickness, drilling distance, and verification hole distance. This paper also conducted a simple correction of the current cross-hole CT method, which increased the average accuracy of the model by 4% and reduced the variability by 3% without increasing the computational complexity. Finally, the analysis confirmed that the model factors for predicting cave height follow a Weibull distribution. The research results can provide theoretical support for karst cave exploration and risk assessment in karst areas.