A PROCEDURE FOR THE PROBABILISTIC ASSESSMENT OF MASONRY STRUCTURES UNDER TSUNAMI

Assessment of Tsunami vulnerability of coastal buildings has gained high interest in the last years for the areas characterized by a high Tsunami hazard. A probabilistic representation of vulnerability is performed by fragility curves, fundamental tools to define possible strategies for risk mitigation. Different prediction approaches can be used for obtaining analytical fragility curves. In this paper, a prediction proposal to be used for masonry structures typical of the Mediterranean coasts based on simplified structural analyses and damage indexes is presented. Different damage states are considered and inundation depth is assumed as input intensity measure. The uncertainties in the demand by the definition of the probability distribution of the inundation scenarios are considered. Further, the uncertainties in the structural capacity are included. Monte Carlo simulations are performed for the scope of this work

Strategies for Waste Recycling: The Mechanical Performance of Concrete Based on Limestone and Plastic Waste

Recycling is among the best management strategies to avoid dispersion of several types of wastes in the environment. Research in recycling strategies is gaining increased importance in view of Circular Economy principles. The exploitation of waste, or byproducts, as alternative aggregate in concrete, results in a reduction in the exploitation of scarce natural resources. On the other hand, a productive use of waste leads to a reduction in the landfilling of waste material through the transformation of waste into a resource. In this frame of reference, the paper discusses how to use concrete as a container of waste focusing on the waste produced in limestone quarries and taking the challenge of introducing plastic waste into ordinary concrete mixes. To prove the possibility of reaching this objective with acceptable loss of performance, the mechanical characteristics of concrete mixed with additional alternative aggregates classified as waste are investigated and dis-cussed in this paper through the presentation of two experimental campaigns. The first experimental investigation refers to concrete made with fine limestone waste used as a replacement for fine aggregate (sand), while the second experimental program refers to the inclusion of three types of plastic wastes in the concrete. Different mixes with different percentages of wastes are investigated to identify possible fields of application. The experimental results indicate that use of limestone quarry waste and use of plastic waste are possible within significant percentage ranges, having recognized a limited reduction of concrete strength that makes concrete itself appropriate for different practical applications

Introduction to the Special Issue on Soft Computing Techniques in Materials Science and Engineering

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Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks

The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk tribometer. The specimens were tested both in untreated state with different hardening levels, and after surface treatment of nitrocarburizing. We concluded that wear maps via ANN modeling were a user-friendly approach for the presentation of wear-related information, since they easily permitted the determination of areas under steady-state wear that were appropriate for use. Furthermore, the achieved optimum ANN model seemed to be a simple and helpful design/educational tool, which could assist both in educational seminars, as well as in the interpretation of the surface treatment effects on the tribological performance of tool steels

Stochastic vulnerability assessment of masonry structures: Concepts, modeling and restoration aspects

A methodology aiming to predict the vulnerability of masonry structures under seismic action is presented herein. Masonry structures, among which many are cultural heritage assets, present high vulnerability under earthquake. Reliable simulations of their response to seismic stresses are exceedingly difficult because of the complexity of the structural system and the anisotropic and brittle behavior of the masonry materials. Furthermore, the majority of the parameters involved in the problem such as the masonry material mechanical characteristics and earthquake loading characteristics have a stochastic-probabilistic nature. Within this framework, a detailed analytical methodological approach for assessing the seismic vulnerability of masonry historical and monumental structures is presented, taking into account the probabilistic nature of the input parameters by means of analytically determining fragility curves. The emerged methodology is presented in detail through application on theoretical and built cultural heritage real masonry structures

Masonry compressive strength prediction using artificial neural networks

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.- (undefined

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices

On the modelling of infilled RC frames through strut models

Infill panels largely affect the seismic response of framed constructions. The wide variety in their mechanical and geometrical features has produced many different models and assumptions in their analytical representation. In this paper the simplest and most diffuse analytical approach, based on the introduction of equivalent struts, has been checked. An overview is presented, focusing on the strut dimensions, strength and number. Two case-studies, taken by two different experimental campaigns, have been considered and reproduced. The obtained results have been compared to the experimental ones, and some parameters have been checked for selecting the model to use for analysis

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype

Equivalent Non-Linearization of Hysteretic Systems by Means of RPS

Background The analysis of elastoplastic systems with hardening (Bouc-Wen systems) under stochastic (seismic) loads needs the evaluation of higher order statistics even in the simplest case of normal distributed input. Objective In this paper, a non-linearization technique is proposed in order to evaluate the moments of any order of the response. Method This technique is developed by means of a nonlinear class of systems whose statistics are a priori known. The parameters of such systems can be chosen in such a way that the two systems are equivalent in a wide sense. Result & Conclusion In the paper, the strategy to obtain the equivalence and the reliability of the results are discussed