Mountain tunnels built near faults often suffer from significant permanent deformation and structural dislocation during seismic activity. In this paper, we present a rock-fault-tunnel geological model with a transition area between the hanging wall and the foot wall which allows the free slippery growth inside the area. A time-sequenced load based on design code and fault activity is conducted in this model to simulate dynamic seismic input after fault dislocation. In our case, a reverse fault with a tunnel cross has been created with this method. A 30cm fault dislocation is simulated by putting the displacement boundary of the hanging wall with a compression vector and the seismic wave is input from the bottom boundary as acceleration waveform adjusted to 0.4g. The model simulates the uplift of the hanging wall and the growth of the slip surface, and reveals the extension mechanism of the triangular shear zone of shear rupture of the surrounding rock due to the extrusion of the reverse fault during the propagation of the reverse fault. The seismic wave with a three-way acceleration was input after the dislocation process. The simulation indicates that with the gradual uplift of the hanging wall, the rock body of the fracture zone shows a more significant large deformation flow trend and a more significant horizontal slip flow. Under reverse fault thrust, the width of the shear effect influence zone is around 300m. A decreasing trend of accumulated strain can be found at the interface due to acceleration input. Dislocation-seismic time-sequence loading may underestimate its damage effects.

In densely populated urban areas, construction spaces are often limited, resulting in insufficient separation between adjacent structures. This issue is compounded by soil-structure interaction (SSI), which significantly alters the dynamic response of buildings. Inadequate separation gaps can lead to increased seismic forces and interstory drift ratios (IDRs), potentially causing significant structural damage and resulting in the loss of life and property. Using inelastic time-history analyses with scaled ground motion records, this research investigated the rapid calculation of seismic gaps between adjacent structures, as well as the impact of SSI on seismic pounding forces and IDRs across different soil conditions, with comparisons made via fixed base. Nonlinear models of 8- and 10-story reinforced concrete (RC) structures, representative of the Turkish building stock, were analyzed in both standalone and adjacent scenarios. Ground motion records were scaled to match the seismic characteristics of Istanbul, a highly earthquake-prone region, and nonlinear time-history analyses were conducted in both horizontal directions simultaneously. This study primarily examined two scenarios. The first scenario involved a rapid assessment based on building height and soil conditions, which can be utilized during the design phase to prevent pounding between adjacent buildings and compared with gap separations calculated using more-complex formulas recommended by regulations. The second scenario addressed the additional shear forces resulting from pounding effects, which must be considered if pounding cannot be avoided. This study proposes a simplified method for determining seismic gaps, aiding practical application for field engineers. This study found that simultaneously considering both SSI and pounding significantly alters dynamic behavior in terms of maximum IDRs and acceleration demands, especially in shorter and lighter structures.

Engineers often estimate the amount of liquefaction-induced building settlements (LIBS) as a performance proxy to assess the potential of earthquake-induced damage to buildings. The first robust LIBS models were initially developed in 2017 and 2018 using traditional statistical approaches. More recently, machine learning techniques have started to be used in developing LIBS models. These recent efforts are a step forward in realizing the potential of machine learning in liquefaction engineering; however, they have often considered only one ML technique for a given dataset and typically used only held-out test sets for model assessment. In this study, five ML-based LIBS models with varying flexibility (i.e., ridge regression, partial least square regression - PLSR, random forest, gradient boosting decision tree - GBDT, and support vector regression) are developed using a LIBS database generated by soil-structure numerical simulations of different buildings and soil profiles shaken by ground motions with varying intensity measures. The motivation for considering models with different flexibility is to include different bias-variance trade-offs. Feature selection with different ML techniques indicates that cumulative absolute velocity, spectral acceleration at one second, contact pressure, foundation width, the thickness of the liquefiable layer, and a shearing liquefaction index are important features in estimating LIBS. The developed ML-based models are assessed considering prediction accuracy in test sets, performance against centrifuge tests and case histories, and trends. The assessment indicates that the random forest, GBDT, and SVR models perform best, providing standard deviation reductions up to 40% relative to a multi-linear regression. Specifically, the random forest and GBDT models exhibit a root mean square error (RMSE) of 0.29 and a coefficient of determination (R2) of 0.93 on test sets, demonstrating a notable improvement compared to a traditional multi-linear regression model, which yields an RMSE of 0.47 and an R2 of 0.82. Moreover, random forest and GBDT, alongside SVR, show a good performance in centrifuge tests and case histories. Finally, given the scarcity of LIBS models, this study also contributes to treating epistemic uncertainties in estimating LIBS, which is ultimately beneficial for performance-based assessments.

This research aims to estimate the expected economic losses associated with the repair cost of a set of codecompliant moment-resisting reinforced concrete buildings located on different soil conditions in Mexico City. The loss assessment methodology is based on the second generation of the Performance-Based Earthquake Engineering (PBEE) framework, which quantifies the seismic performance of buildings in terms of metrics such as economic losses, downtime, and casualties, which are more meaningful to owners and stakeholders for the decision-making process. The methodology uses a probabilistic approach that takes into account uncertainties in seismic intensity, structural response, component damage, and consequence prediction. The seismic response of structures was calculated in terms of inter-story drifts and floor accelerations to assess the damage to their structural and non-structural components. In addition, taking into account the seismic hazard of the site, the expected annual losses (EAL) are calculated to provide insight into the seismic performance evaluation of structures with different characteristics in terms of the financial impact in seismic-prone regions like Mexico City.