This paper explores the use of Artificial Neural Networks (ANN) for the rocking problem. The paper adopts rigid rocking blocks of different sizes and slenderness, which undergo rocking motion without sliding and bouncing when subjected to recorded earthquakes. This research focuses on the cases where the blocks overturn or safely return to their initial (rest) position at the end of the ground shaking. An ANN model is trained to efficiently categorise the response into overturning or safe rocking using the structural parameters, ground motion characteristics, and the coefficient of restitution as input. The results show the substantial contribution of velocity and frequency characteristics of the ground motion to overturning. In addition, ANN is used to predict the response amplitude and identify the most critical input variables that govern safe rocking. The analysis reveals that rocking amplitude is governed by a combination of duration, frequency, and intensity characteristics of the ground excitation. Interestingly, the maximum incremental velocity (MIV), a novel intensity measure for the rocking literature, shows a substantial correlation with the rocking amplitude. In this context, this paper proposes closed‐form expressions using the most influential input variables to provide a quick, yet adequately accurate, response prediction. Finally, this study pays special attention to the contribution of the coefficient of restitution, which, in general, is less critical to the peak safe rocking response, while it becomes more important to the overturning response.

In this paper, two theoretical models, micro and macro model, are presented to investigate the effect of circumferential cracks on the structural characteristics of a pipe. In the micro model, the equivalent bending stiffness is obtained based on the energy method and fracture mechanics. Because of the high change of strain energy near the crack location, the variation is represented by reduction in the bending stiffness along the length of the pipe. The development of the macro model is based on a simplified procedure, i.e., the pipe is separated into two segments without any change in the cross section property. Two separated pieces are connected by a rotational massless spring of the infinitesimal length inserted at the crack location. Moreover, it is shown that in the macro model so smaller number of elements are required than the micro model to provide a sufficient accuracy. Finally, the experimental measurements are utilized to validate the proposed models. The deflections and frequencies obtained for several cases through the displacement and vibration method, using the proposed models agree reasonably well with the experimental measurement data.

Double-layer barrel vault roofs with double-layer vertical walls are widely used in important public buildings because of their high structural efficiency, favorable stiffness-to-weight ratio, and architectural versatility. Although incremental dynamic analysis (IDA) is a widely accepted approach for seismic assessment, it requires numerous nonlinear time-history analyses (NTHAs), resulting in high computational cost. This study presents an artificial neural network (ANN)-based surrogate modeling framework to accurately predict the seismic response of these structural systems, reducing the need for repeated NTHAs, enabling rapid estimation of structural dynamic responses, and facilitating direct development of seismic fragility curves. The proposed framework substantially decreases computational effort while maintaining an effective balance between accuracy and efficiency. A comprehensive seismic damage database is first generated using finite element (FE) models developed in OpenSees. Fragility curves are then obtained using both the conventional IDA procedure and the proposed ANN-based surrogate approach. Results show that the ANN surrogate accurately predicts the responses of structures subjected to scaled ground motions and effectively captures their nonlinear seismic behavior. Furthermore, the resulting fragility curves closely match those from the conventional IDA method, demonstrating the accuracy, reliability, and efficiency of the proposed framework for rapid seismic assessment of double-layer barrel vault structures with double-layer walls.

Ground motion models (GMMs) constitute a fundamental component of the probabilistic seismic hazard analysis (PSHA) framework and represent one of the primary sources of epistemic uncertainty in seismic hazard estimates. This uncertainty largely stems from the availability of multiple candidate GMMs, with varying performance across different ground motion databases. The logic-tree framework is a well-established method for addressing this uncertainty by incorporating multiple GMMs with logic-tree branch weights. However, selecting an appropriate suite of GMMs is a non-trivial task. While often determined by expert judgment, the inherent subjectivity of this process has motivated the development of data-driven ranking methods. This study evaluates parametric and machine-learning-based GMMs for Türkiye using two information-theoretic ranking methods: the log-likelihood (LLH) and the deviance information criterion (DIC). The analysis uses an updated Turkish strong-motion database comprising 12,160 recordings from 1976 to 2023, including observations from the 2023 earthquakes. The DIC framework employs Markov chain Monte Carlo (MCMC) sampling to estimate the posterior distribution of the total residual standard deviation while keeping the median prediction functions and regression coefficients fixed. Ranking stability is further investigated across magnitude ranges and event-based subsets, alongside sensitivity of DIC to prior distributions.

An analytical method for diagnosis of cracks in thick-walled pipes with a circular hollow section is investigated in this study. In the proposed method, the defect is assumed to be a non-leaking crack, which is modeled by a massless linear spring with infinitesimal length at the crack location. In order to find the cracks in the pipe, the vibration-based method related to the modal properties of the pipe is utilized. In the modal analysis, the mass and stiffness matrices influence the dynamic properties of the pipe. It is assumed that the mass matrix remains unchanged after the crack initiation, while the corresponding stiffness matrix changes. The stiffness matrix of a cracked element can be formulated by the finite element method with two unknown parameters: location and depth of the crack. Using the eigensolution for an undamped dynamic system to formulate the objective function yields to a complicated optimization problem, which can be solved by an iterative numerical optimization method. Among the optimization approaches, the Artificial Bee Colony (ABC) algorithm is a simple and flexible technique for minimizing the objective function. In this paper, the analytical model is utilized to find the size and position of cracks in a pipe using the ABC algorithm and subsequently some numerical examples are examined in order to assess the accuracy of the method. The results show that the proposed method is able to acceptably estimate the location and depth of multiple cracks in the straight pipes as well as curved ones.