Solution Properties for Pertubed Linear and Nonlinear Integrals Equations

In this study we consider perturbative series solution with respect to a parameter {\epsilon} > 0. In this methodology the solution is considered as an infinite sum of a series of functional terms which usually converges fast to the exact desired solution. Then we investigate perturbative solutions for kernel perturbed integral equations and prove the convergence in an appropriate ranges of the perturbation series. Next we investigate perturbation series solutions for nonlinear perturbations of integral equations of Hammerstein type and formulate conditions for their convergence. Finally we prove the existence of a maximal perturbation range for non linear integral equations

Seismic analysis and risk mitigation of existing constructions

Following a thorough and lengthy procedure, we would like to thank all contributors for their highest calibre papers, which comprise the Special Issue on \u201cSeismic analysis and risk mitigation of existing constructions\u201d of the Open Construction and Building Technology Journal. The topic of the Special Issue encompasses a large number of issues spanning the design of special interventions for the reduction of the effects of earthquakes on civil structures and infrastructures, to the structural identification and assessment issues. The field of seismic engineering is continuously looking for new strategies and methods, which empower the designers and make them able to obtain more accurate response predictions. Researchers are involved in this process and are called to successfully encounter new challenges emerging from the increasing need for the assessment of existing constructions, especially when assuming strategic roles. As is also reflected by the papers presented in the Special Issue, the continuous advances of the research in this field moves across two basic directions. On the one hand, there is the direction of the robustness and the reliability of the recent nonlinear seismic assessment methods (static, dynamic, incremental dynamic). Several approaches can be followed to predict the response of structures to strong ground motions; however the results coming from each of them are in some cases conflicting and not always amenable to easy interpretation. On the other hand, the reliability of structural models still remains a major task of structural engineering and of seismic engineering in particular. Mathematical models have to reproduce the physics of structures and its evolution during complex damaging processes. Global and local models tend to reflect this by minimizing the loss of information. In the Special Issue, we are proud to present state-of-the-art research findings described in detail in 9 papers authored by 27 researchers of different universities in Italy, California (USA), Greece and United Kingdom. The papers deal with the seismic analysis and risk mitigation aiming to address different purposes by proposing numerical, analytical approaches and experimental tests

A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs

When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R2) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R2 and RMSE values were obtained as 0.9476–0.9831 and 14.4965–24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs

Definition of seismic vulnerability maps for civil protection systems: The case of lampedusa Island

The opportunity to locate and quantify the major criticalities associated to natural catastrophic events on a territory allows to plan adequate strategies and interventions by civil protection bodies involved in local and international emergencies. Seismic risk depends, most of all, on the vulnerability of buildings belonging to the urban areas. For this reason, the definition, by a deep analysis of the territory, of instruments identifying and locating vulnerability, largely favours the activities of institutions appointed to safeguard the safety of citizens. This paper proposes a procedure for the definition of vulnerability maps in terms of vulnerability indexes and critical peak ground accelerations for mid-small urban centres belonging to Mediterranean areas. The procedure, tested on the city centre of the Island of Lampedusa, is based on a preliminary historical investigation of the urban area and of the main formal and technological features of buildings involved. Moreover, the vulnerability of the constructions is evaluated by fast assessment methods (filling of evaluation forms). The vulnerability model, allowing the definition of the fragility curves, is calibrated on the basis of the results of an identification process of prototype buildings, selected to be adequately representative. Their characterizations have been provided using the results of an experimental dynamic investigation to develop high representative numerical model. Critical PGA values have been determined by pushover analyses. The results presented provided an unambiguous representation of the major criticalities with respect to seismic vulnerability and risk, of the city centre of the island, being a suitable tool for planning and handling of emergencies

Rock-burst occurrence prediction based on optimized naïve bayes models

Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE

A gene expression programming model for predicting tunnel convergence

Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many uncertainties. Tunnel convergence, as the tendency for reduction of the excavated area due to change in the initial stresses, is frequently observed, in order to monitor the safety of construction and to evaluate the design and performance of the tunnel. This paper presents a model/equation obtained by a gene expression programming (GEP) algorithm, aiming to predict convergence of tunnels excavated in accordance to the New Austrian Tunneling Method (NATM). To obtain this goal, a database was prepared based on experimental datasets, consisting of six input and one output parameter. Namely, tunnel depth, cohesion, frictional angle, unit weight, Poisson's ratio, and elasticity modulus were considered as model inputs, while the cumulative convergence was utilized as the model's output. Configurations of the GEP model were determined through the trial-error technique and finally an optimum model is developed and presented. In addition, an equation has been extracted from the proposed GEP model. The comparison of the GEP-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of GEP modeling to estimate the tunnel convergence in a reliable, robust, and practical manner

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 discussed 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.peer-reviewe

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

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