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Recent earthquakes clarified that existing a soft/weak-story in a building could completely invert the failure mechanisms of the structure. Several studies were implemented to evaluate the potential risk subjected to the buildings under the earthquake hazard. However, these researchers discarded the effect of soft/weak-story on the demand parameters of their models. This paper presents a rapid demand estimation framework for generic buildings considering the effect of soft/weak-story. In this regard, the simplified model developed according to the HAZUS approach is rectified to apply the effect of soft/weak-story on the structural behavior of the generic buildings. Artificial neural networks are implemented to remove the required time-consuming nonlinear response history analyses from the post-earthquake actions. This research utilized a suite of 111 earthquake records, originally developed by the SAC project. These motions are uniformly scaled from 0:1g to 1:5g to obtain a generalized dataset with different intensities. Bayesian optimization algorithm is conducted to achieve a prediction model with optimized hyperparameters. Results clarify that the proposed method is reliable and computationally cost-effective in predicting the demand parameters of generic buildings. This framework can be used in the body of a risk assessment platform to facilitate the emergency response to the earthquake hazard. [ABSTRACT FROM AUTHOR]

Recent earthquakes illustrated that damage to highway bridges, as one of the critical components of transportation systems, could lead to irreparable social and economic losses. Identifying the extent of probable damage to primary and secondary components of the bridges in the shortest time after a seismic event can significantly reduce the losses related to delays in emergency responses. This paper presented a rapid Machine Learning (ML)-based damage detection framework to detect the extent of damage exposed to primary and secondary components of reinforced concrete bridges under earthquake motions. The proposed algorithm used a set of 160 pair of records, originally developed for seismic performance evaluation of highway bridges, to generate a generalized dataset. These records were uniformly scaled to 16 different peak ground acceleration values ranging from 0.05 g to 1.6 g to produce a wide range of ground motion intensities. This study applied damage indicators extracted from the acceleration time histories as the input attributes to the ML algorithm. The acceleration signals were polluted to a maximum level of 10% noise to simulate the field condition. Two different bridge models (i.e. a two-dimensional multi-span concrete box-girder and a three-dimensional multi-span continuous I-girder bridge) were used to validate the proposed technique. A parametric study was implemented to determine the most efficient ML algorithm for determining the level of damage in the bridge's components. Bayesian Optimization (BO) algorithm was conducted to tune the hyperparameters of each ML algorithm. Results indicate that Support Vector Machines (SVMs) are the most accurate learners compared to the Naive Bayes, decision tree, discriminant analysis, and K-nearest neighbor for both primary and secondary components. [ABSTRACT FROM AUTHOR]

Summary: Rapid health assessment of essential buildings such as hospitals, fire stations, and large residential complexes is crucial after damaging earthquakes. The use of advanced technologies such as wireless sensors, learning algorithms, and signal processing methods became more attractive in such fast applications due to their higher reliabilities and efficiencies compared to the conventional visual inspection methods. This paper presents a robust post‐earthquake damage detection framework for predicting the extent and location of damage occurrence in the braced‐frame structures after an earthquake. To do so, features derived from acceleration response of the structure were used along with a classification learner to determine the health condition of the structure. Decision tree classifiers are used for the purpose of damage classification where the Bayesian optimization algorithm is implemented to optimize the architecture of the mentioned classifier. A one‐story chevron steel‐braced frame, a three‐story X‐braced steel‐frame, and a five‐story three‐dimensional building are considered to validate the proposed method. The total number of 3774 and 1887 nonlinear response history analyses were respectively performed for 2D and 3D numerical models under scaled SAC motions, using the OpenSees simulation platform. Furthermore, in order to simulate the field condition, a maximum level of 10% white Gaussian noise is added to the output signals. Results obtained from the three case studies show that the proposed framework is robust and reliable in predicting the extent of damage level in the braced‐frame structures in a short time after an earthquake. [ABSTRACT FROM AUTHOR]

Dams are essential infrastructures as they provide a range of economic, environmental, and social benefits to the local populations. Damage in the body of these structures may lead to an irreparable disaster. This paper presents a cost-effective vibration-based framework to identify the dynamic properties and damage of the dams. To this end, four commonly occurred damage scenarios, including (1) damage in the neck of the dam, (2) damage in the toe of the structure, (3) simultaneous damage in the neck and the toe of the dam, and (4) damage in the lifting joints of the dam, are considered. The proposed method is based on processing the acceleration response of a gravity dam under ambient excitations. First, the random decrement technique (RDT) is applied to determine the free-vibration of the structure using the structural response. Then, a combined method based on Hilbert–Huang Transform (HHT) and Wavelet Transform (WT) is presented to obtain the dynamic properties of the structure. Next, the cubic-spline technique is used to make the mode shapes differentiable. Finally, Continuous Wavelet Transform (CWT) is applied to the residual values of mode shape curvatures between intact and damaged structures to estimate the damage location. In order to evaluate the efficiency of the proposed method in field condition, 10% noise is added to the structural response. Results show promising accuracy in estimating the location of damage even when the structure is subjected to simultaneous damage in different locations. [ABSTRACT FROM AUTHOR]