Although the principal aim of the rockfall management is to prevent rock boulders from reaching the buildings instead of the buildings resisting the boulder impacts, there usually exists a residual risk that has to be assessed, even when structural protection measurements are taken. The evaluation of the expected damage of buildings due to rockfalls using empirical data from past events is not always possible, as transferring and applying damage observations from one area to another can be unrealistic. In order to simulate potential rockfall scenarios and their damage on buildings, numerical methods can be an alternative. However due to their increased requirements in expertise and computational costs, their integration into the risk analysis is limited, and simpler tools to assess the rockfall vulnerability of buildings are needed. This paper focuses on the application of artificial intelligence AI methods for providing the expected damage of masonry walls which are subjected to rockfall impacts. First, a damage database with 672 datasets was created numerically using the particle finite element method and the finite element method. The input variables are the rock volume (VR), the rock velocity (RV), the masonry wall (t) and the masonry tensile strength fm. The output variable is a damage index (DI) equal to the percentage of the damaged wall area. Different AI algorithms were investigated and the ANN LM 4-21-1 model was selected to optimally assess the expected wall damage. The optimum model is provided here (a) as an analytical equation and (b) in the form of contour graphs, mapping the DI value. Known the VR and the RV, the DI can be directly used as an input for the vulnerability of masonry walls into the quantitative rockfall risk assessment equation.
This study presents an efficient finite element analysis technique which shows great versatility in analysing complex discontinuous systems subjected to static, dynamic, or seismic loadings. The method incorporates discontinuities in the analysis of discontinuous structures by the use of interface elements designed to simulate the actual behaviour at the interfaces between contacting materials. Several case-problem studies that exhibit discontinuous behaviour have been performed in order to demonstrate the potential and applicability of the proposed method of analysis. One of these studies is reported in Part 2 of this paper, where a non-linear model for the analysis of unreinforced masonry walls is presented. Response results obtained, demonstrate that the overall response of a discontinuous system to external loading is significantly affected by behaviour at the interfaces between contacting materials. Through the inclusion of discontinuities with particular measurable properties, the proposed method of analysis conforms better to actual conditions than do other methods where a continuum is assumed.
The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.
In this paper, the description of a series of quasi-static pushing tests and dynamic snap-back tests is proposed, involving the base-isolated emergency building of the Palermo university hospital. The base isolation system is characterized by a set of double-curved friction pendulum isolators placed on the top of the columns of the underground level, characteristics that cannot be found in the experimental studies available in the literature. The aim of the work was to investigate the static and dynamic properties of the building in question and comparing the in-situ results with the characteristics assigned during the design process and to assess the level of agreement. Static lateral pushing tests were aimed at identifying the main mechanical properties of the whole isolation level (e.g. friction forces and stiffness). Moreover, dynamic snap-back tests were carried out for different levels of displacement to characterize the dynamics of the building and the re-centering capacity of the isolation system. The design of the field testing, comprising the thrust mechanical device and the reaction RC wall, is described along the paper followed by the description of the in-situ testing and its main results. Then, the role of the in situ tests in proving the structural reliability and how to choose/use them in the case of friction pendulum base-isolated structures are discussed.
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