A novel heuristic algorithm for the modeling and risk assessment of the covid-19 pandemic phenomenon

The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the quality of the health system in each area, the age distribution of population, geographical and environmental factors as well as other conditions. Based on derived predicted epidemic curves, a new 3D-epidemic surface is proposed to assess the epidemic phenomenon at any time of its evolution. This research highlights the potential of the proposed model as a tool which can assist in the risk assessment of the COVID-19. Mapping its development through 3D-epidemic surface can assist in revealing its dynamic nature as well as differences and similarities among different districts

Seismic and Restoration Assessment of Monumental Masonry Structures

Masonry structures are complex systems that require detailed knowledge and information regarding their response under seismic excitations. Appropriate modelling of a masonry structure is a prerequisite for a reliable earthquake-resistant design and/or assessment. However, modelling a real structure with a robust quantitative (mathematical) representation is a very difficult, complex and computationally-demanding task. The paper herein presents a new stochastic computational framework for earthquake-resistant design of masonry structural systems. The proposed framework is based on the probabilistic behavior of crucial parameters, such as material strength and seismic characteristics, and utilizes fragility analysis based on different failure criteria for the masonry material. The application of the proposed methodology is illustrated in the case of a historical and monumental masonry structure, namely the assessment of the seismic vulnerability of the Kaisariani Monastery, a byzantine church that was built in Athens, Greece, at the end of the 11th to the beginning of the 12th century. Useful conclusions are drawn regarding the effectiveness of the intervention techniques used for the reduction of the vulnerability of the case-study structure, by means of comparison of the results obtained

Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials

This work presents a soft-sensor approach for estimating critical mechanical properties of sandcrete materials. Feed-forward (FF) artificial neural network (ANN) models are employed for building soft-sensors able to predict the 28-day compressive strength and the modulus of elasticity of sandcrete materials. To this end, a new normalization technique for the pre-processing of data is proposed. The comparison of the derived results with the available experimental data demonstrates the capability of FF ANNs to predict with pinpoint accuracy the mechanical properties of sandcrete materials. Furthermore, the proposed normalization technique has been proven effective and robust compared to other normalization techniques available in the literature

Modeling of surface roughness in electro-discharge machining using artificial neural networks

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components

A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon

This article belongs to the special issue: Soft computing techniques in materials science and engineeringSummarization: The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the quality of the health system in each area, the age distribution of population, geographical and environmental factors as well as other conditions. Based on derived predicted epidemic curves, a new 3D-epidemic surface is proposed to assess the epidemic phenomenon at any time of its evolution. This research highlights the potential of the proposed model as a tool which can assist in the risk assessment of the COVID-19. Mapping its development through 3D-epidemic surface can assist in revealing its dynamic nature as well as differences and similarities among different districts.Παρουσιάστηκε στο: Computer Modeling in Engineering & Science

Mapping and holistic design of natural hydraulic lime mortars

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cemconres.2020.106167.In recent years, the study of high hydraulicity natural hydraulic lime (NHL5) mortars has been in the focus of many researchers, as it is considered a compatible, eco-friendly binding material, which can be used both for the restoration of culturally and historically significant structures, as well as for the construction of contemporary buildings. In the present study, artificial neural networks (ANNs) are used, aiming to simulate and map the development of NHL5 mortars' characteristics, such as compressive strength (CS), ratio of compressive to flexural strength (CS/FL) and consistency (CO), for selected mortar mix parameters, namely the binder to sand ratio (B/S), the water to binder ratio (W/B) and the maximum diameter of the aggregate (MDA) for different mortar specimen ages (AS). To this purpose, databases were developed, integrating experimental data from the international literature. Experimental verification of the developed ANN models revealed satisfactory fitting between theoretical and experimental results. This research highlights the potential of ANNs as a tool which can assist in mortar design and/or optimization, while mapping the development of mortar characteristics can assist in revealing the influence of the different mortar mix parameters on each characteristic. Furthermore, by combining the results of the three developed ANNs (CS, CO, CS/FL) targeted multi-parametric design of mortars can be assisted through a novel approach.The authors would like to thank Jose Ignacio Alvarez Galindo, Professor at the University of Navarra, Spainand Binh Thai Pham, Professor at University of Transport Technology, Hanoi, Vietnam, for their valuable comments and discussions. The authors would also like to express their acknowledgement to graduate students Chrysoula Karamani, Athanasia Skentou and Ioanna Zoumpoulaki for their assistance on the computational implementation of the ANN models