Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise.As a result,a robust,reliable,and repeatable method of damage identification is required.Ensemble learning algorithms for identifying structural damage are evaluated in this article,which use deep convolutional neural networks,including simple averaging,integrated stacking,separate stacking,and hybridweighted averaging ensemble and differential evolution(WAE-DE)ensemblemodels.Damage identification is carried out on three types of damage.The proposed algorithms are used to analyze the damage of 4585 structural images.The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix.For the testing dataset,the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model(WAE-DE)in distinguishing damage types as flexural,shear,combined,or undamaged.

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

Soft computing(SC)refers to the ability of a digital computer or robot to perform functions that are normally associated with intelligent individuals,such as reasoning and problem-solving.An example of this would be a project aimed at creating systems capable of reasoning,discovering meaning,generalising,or learning from past experience.Science and engineering problems that are both non-linear and complex can be solved using these methodologies.It has been proven that these algorithms can be used to solve numerous real-world problems.The techniques outlined can be used to increase the accuracy of existing models/equations,or they can be used to propose a newmodel that can address the problem.

A Novel Heuristic Algorithm for theModeling and Risk Assessment of the COVID-19 Pandemic Phenomenon

Themodeling 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.