Leonardo M. Massone

Chile, recognized as one of the world’s most earthquake-prone nations, has gained valuable insights from significant earthquakes, such as those in 1985 and 2010, which have influenced updates to the nation’s design codes. Although Chile’s seismic design approach has been largely effective in recent earthquakes and demonstrated an “operational” performance level in most structures, performance-based design (PBD) methods have not yet been officially incorporated as valid approaches in the Chilean seismic design codes for buildings. However, in 2017, the Chilean Association on Seismology and Earthquake Engineering (ACHISINA) introduced a PBD approach, primarily for verification purposes, based on the Los Angeles Tall Buildings Structural Design Council (LATBSDC) framework. In this work, firstly, we provide an overview of Chile’s PBD methodology, focusing on the thresholds for various performance levels. These levels are established through experimental and numerical analysis, correlating performance with permissible damage levels. The second part of the paper examines the seismic performance of a residential building, designed before the 2010 Maule earthquake and subsequently damaged, using Chile’s PBD guidelines. This case study highlights the implementation and effectiveness of PBD for assessing seismic resilience in Chilean structures.

This study investigates the experimental response of cylindrical specimens made of plain concrete and concrete reinforced with PVA fibers that were subjected to low-cycle fatigue tests under compression. Two types of PVA fiber were used in the experiments, with lengths of 12 and 15mm, and three volume levels were considered: 0%, 1%, and 2%. The experimental program included tests where the fatigue load was applied during the pre-crushing stage of the sample (pre-peak), as well as after the onset of strength degradation (post-peak). In the pre-peak compression fatigue tests, which are more common in the literature, a maximum stress level of 95% of the specimen strength was applied. The post-peak compression fatigue tests, which are not available in the literature and could help understand the durability of concrete with fibers under cyclic response, were conducted at two levels of strength degradation, measured with respect to the monotonic compressive strength: 2% and 10%. In addition, three levels of maximum compressive stress for the cycles were used: 85%, 90%, and 95%. Based on the results obtained, fatigue models (Wöhler) and models of secondary strain rate versus fatigue life were developed. Furthermore, the envelope of maximum strains at low cycle fatigue failure was analyzed for both pre-peak and post-peak tests.

Reinforced concrete structural walls (RCSWs) are one of the most efficient lateral force-resisting systems used in buildings, providing sufficient strength, stiffness, and deformation capacities to withstand the forces generated during earthquake ground motions. Identifying the failure mode of the RCSWs is a critical task that can assist engineers and designers in choosing appropriate retrofitting solutions. This study evaluates the efficiency of three ensemble deep neural network models, including the model averaging ensemble, weighted average ensemble, and integrated stacking ensemble for predicting the failure mode of the RCSWs. The ensemble deep neural network models are compared against previous studies that used traditional well-known ensemble models (AdaBoost, XGBoost, LightGBM, CatBoost) and traditional machine learning methods (Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest). The weighted average ensemble model is proposed as the best-suited prediction model for identifying the failure mode since it has the highest accuracy, precision, and recall among the alternative models. In addition, since complex and advanced machine learning-based models are commonly referred to as black-box, the SHapley Additive exPlanation method is also used to interpret the model workflow and illustrate the importance and contribution of the components that impact determining the failure mode of the RCSWs.