Numerical Model Validation for Detection of Surface Displacements over Twin Tunnels from Metro Line 1 in the Historical Area of Seville (Spain)

In order to solve connectivity problems in metropolitan areas, the development of underground metro lines constitutes an unquestionable requirement. However, the construction work thereof encounters unfavourable circumstances when surface excavations must be carried out that cross historical areas of the city, due to the need to control surface movements. The design of the metro in the city of Seville (Spain) from 2004 to 2006 provides a representative example of this situation and triggered major upheavals that exerted repercussions on historical buildings. For these reasons, the excavation stages of Line 1 of this metro have been simulated by numerical methods using FLAC3D software and validated with the results provided by the real conditions. Consequently, various surface settlements have been evaluated by taking not only variates of the main parameters that characterise the soil of Seville, but also of the various load situations and excavation conditions. Notable results have been achieved through calibration of 54 variants of the same model corresponding to Line 1, and their comparison with the real results obtained in nine critical areas of the itinerary. The results obtained have made it possible to determine the effects of excavation on the subsoil of the city of Seville with great accuracy, since the percentage error of calculated vertical surface movements varies from 0.1% to 5.3%

A svr-gwo technique to minimize flyrock distance resulting from blasting

Flyrock is one of the most important environmental and hazardous issues in mine blasting, which can affect equipment and people, and may lead to fatal accidents. Therefore, prediction and minimization of this phenomenon are crucial objectives of many rock removal projects. This study is aimed to predict the flyrock distance with the use of machine learning techniques. The most effective parameters of flyrock were measured during blasting operations in six mines. In total, 262 data samples of blasting operations were accurately measured and used for approximation purposes. Then, flyrock was evaluated and estimated using three machine learning methods: principle component regression (PCR), support vector regression (SVR), and multivariate adaptive regression splines (MARS). Many models of PCR, SVR, and MARS were constructed for the flyrock distance prediction. The modeling process of each method is elaborated separately in a way to be used by other researchers. The most important parameters affecting these models were assessed to obtain the best performance for the developed models. Eventually, a preferable model of each machine learning technique was used for comparison purposes. According to the used performance indices, coefficient of determination (R2), and root mean square error, the SVR model showed a better performance capacity in predicting flyrock distance compared with the other proposed models. Thus, the SVR prediction model can be used to accurately predict flyrock distance, thereby properly determining the blast safety area. Additionally, the SVR model was optimized by new optimization algorithm namely gray wolf optimization (GWO) for minimizing the flyrock resulting from blasting operation. By developing optimization technique of GWO, the value of flyrock can be decreased 4% compared with the minimum flyrock distance

Spatio-temporal prediction surface displacement in urban underground excavation: a case study in Seville

One of the primary challenges in excavating underground in urban areas is controlling and mitigating ground surface displacement caused by Earth Pressure Balance (EPB) tunneling. It is crucial to avoid damaging historical monuments and buildings in these areas. This paper presents a new method for predicting the surface displacement caused by EPB in Seville. A spatiotemporal dataset was generated for this study using numerical simulation in FLAC3D. The simulation replicates the excavation process of the Seville metro line in real-time, and records the surface displacements at selected points in the dataset. The last 20-time steps of excavation are predicted, and the first 80-time steps are chosen for training and tuning hyperparameters, as the dataset is spatiotemporal. A recurrent neural network (RNN) is used to detect and predict patterns between surface displacement and input features at different time steps and locations of the excavation. After fine-tuning the RNN, the model achieved an accuracy of 0.91 for the evaluated R-squared (R2), indicating its practicality for real-time prediction of surface displacement in underground excavations in Seville. The model's performance can be further improved with a larger data range. By deploying it as a hazard detector, the model can issue a warning if the ground displacement exceeds the limit, thereby preventing potential hazards. This approach can help control and mitigate potential hazards in underground excavations in historical cities

Numerical Model Validation for Detection of Surface Displacements over Twin Tunnels from Metro Line 1 in the Historical Area of Seville (Spain)

In order to solve connectivity problems in metropolitan areas, the development of underground metro lines constitutes an unquestionable requirement. However, the construction work thereof encounters unfavourable circumstances when surface excavations must be carried out that cross historical areas of the city, due to the need to control surface movements. The design of the metro in the city of Seville (Spain) from 2004 to 2006 provides a representative example of this situation and triggered major upheavals that exerted repercussions on historical buildings. For these reasons, the excavation stages of Line 1 of this metro have been simulated by numerical methods using FLAC3D software and validated with the results provided by the real conditions. Consequently, various surface settlements have been evaluated by taking not only variates of the main parameters that characterise the soil of Seville, but also of the various load situations and excavation conditions. Notable results have been achieved through calibration of 54 variants of the same model corresponding to Line 1, and their comparison with the real results obtained in nine critical areas of the itinerary. The results obtained have made it possible to determine the effects of excavation on the subsoil of the city of Seville with great accuracy, since the percentage error of calculated vertical surface movements varies from 0.1% to 5.3%