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

Monte Carlo analysis of masonry structures under tsunami action: Reliability of lognormal fragility curves and overall uncertainty prediction

Tsunami vulnerability of coastal buildings has gained more and more interest in recent years, in the consciousness of what losses may be caused. The improvement of the available approaches for the quantitative estimation of the probability of building damage and for defining possible strategies for risk mitigation is an actual goal. In this framework, several authors have provided empirical fragility curves based on field surveys after tsunamis. Nevertheless, a predictive approach based on analytical fragility curves, which can be extended to many classes of buildings, is essential for the scopes of civil protection and risk mitigation. In this paper, an approach for the construction of fragility curves, proposed for masonry structures under tsunami waves, is discussed and refined in the part regarding the assignment of the uncertainties. Further, an assessment of the reliability of the lognormal fragility distribution is carried out based on a Monte Carlo simulation applied to 4 classes of buildings. Here, it is shown that Monte Carlo analysis allows a direct evaluation of the uncertainties without the need to resort to ambiguous regression analyses and rules of combination of the uncertainties of demand and capacity based on the regression analysis results or other uncertainty estimation approaches

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

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 deep dive into tunnel blasting studies between 2000 and 2023-A systematic review

Tunnel blasting is a common practice used to excavate rock formations. Many academic research articles have emerged and burgeoned in the field of tunnel blasting. These articles are dedicated to investigating objectives such as blasting vibration, rock damage, and vibration energy individually. However, no systematic analysis is conducted to consolidate and analyze the findings from the literature related to tunnel blasting. This study addresses this by offering a systematic review to explore the state of tunnel blasting research. A science mapping approach using bibliometric analysis is employed to examine 144 peer-reviewed journal articles. The review identified the most influential journals, institutions, researchers, and articles on tunnel blasting research, and it also summarizes the research hotspots of tunnel blasting according to the cluster analysis of research keywords. Findings in this review revealed the contribution of two leading journals, three leading institutions, and three leading researchers on the research of tunnel blasting. Moreover, four research keywords, i.e., blasting vibration, numerical simulation, rock damage, and overbreak, were identified as the research hotspots in 2018–2023. Finally, this review also speculated the future research directions/avenues of tunnel blasting, aiming to bring to light the deficiencies in the currently existing research and provide paths for future research

Analyzing surface settlement factors in single and twin tunnels : A review study

Surface settlement (SS) resulting from tunnel excavation operations is a critical concern in tunnel engineering due to its potential impact on adjacent structures. This review synthesizes current knowledge on factors influencing SS induced by tunneling activities, focusing on tunnel geometry, soil properties, and operational parameters. Empirical formulas, numerical analyses, and machine learning (ML) techniques are examined for the effectiveness in predicting SS, highlighting the limitations and potential. Key findings underscore the significant influence of tunnel geometry, soil properties and tunnel operational parameters on SS outcomes. However, limitations exist in current studies, including the lack of consideration for diverse soil types and operational parameters like jack force thrust and penetration rate. The study underscores the importance of proper management of tunneling operations, including optimizing face pressure, to mitigate SS risks. Practical implications for practicing engineers include thorough site investigations, risk assessments and comprehensive monitoring programs. Leveraging historical data and ML algorithms can enhance SS prediction accuracy and aid in proactive risk management. Ultimately, mitigating SS risks is crucial for safeguarding existing infrastructure in congested urban areas