Optimization is the key to obtaining efficient utilization of resources in structural design. Due to the complex nature of truss systems, this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints. Two new algorithms, the Red Kite Optimization Algorithm (ROA) and Secretary Bird Optimization Algorithm (SBOA), are utilized on five benchmark trusses with 10, 18, 37, 72, and 200-bar trusses. Both algorithms are evaluated against benchmarks in the literature. The results indicate that SBOA always reaches a lighter optimal. Designs with reducing structural weight ranging from 0.02% to 0.15% compared to ROA, and up to 6%-8% as compared to conventional algorithms. In addition, SBOA can achieve 15%-20% faster convergence speed and 10%-18% reduction in computational time with a smaller standard deviation over independent runs, which demonstrates its robustness and reliability. It is indicated that the adaptive exploration mechanism of SBOA, especially its Levy flight-based search strategy, can obviously improve optimization performance for low-and highdimensional trusses. The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA, a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.

The seismic performance of reinforced concrete (RC) frames with masonry infill walls is strongly affected by the presence of openings, which are unavoidable for architectural reasons. While previous research has often neglected or simplified the role of openings, this study presents a systematic parametric investigation of infilled RC subassemblies with both window- and door-type openings. The analysis explicitly considers opening size (Ao/Ai ratio), position (central vs. eccentric), and the presence of vertical confining elements (ties/serklaži). Using a calibrated micromodelling framework, the study quantifies component-wise shear resistance contributions of the RC frame, masonry infill, and confinement across different drift levels associated with EMS-98 damage grades. Results show that opening size and eccentricity substantially influence stiffness degradation and load transfer, with eccentric door openings imposing shear demands on the RC frame that often exceed the bare-frame design capacity. Vertical confinement proves effective at early damage stages (DG1–DG2), delaying infill degradation and enhancing out-of-plane stability, but its relative contribution remains below 20

An increasing number of coastal communities across Italy and the entire globe are exposed to tsunami hazard, also due to climate change. Tsunamis can be triggered by several events such as earthquakes, volcanic eruption and submarine landslides. Previous research conducted on tsunami risk assessment highlighted the absence of specific frameworks able to assess the economic and social loss and to quantify the potential effect of large-scale tsunami mitigation strategies on Italian coastal regions. The present paper summarizes the main objectives of the PRIN (Projects of great national interest) MITICO - MItigation of Tsunami Impact on COastal Regions project, that aims to develop a framework to perform a reliable tsunami risk and loss assessment for Italian coastal communities also accounting for the presence of large-scale mitigation strategies. A state-of-the-art on the effect of mitigation strategies on tsunami loads on structures, the vulnerability of buildings to tsunami and the direct loss analysis will be outlined thought the paper, and advancements will be presented based on the ongoing project research activities.

This paper aims to develop and evaluate the one dimensional convolutional neural network (1D-CNN) deep learning (DL) model for earthquake accelerogram time series classification, with the ultimate goal of predicting seismic limit state exceedance in Jacket Type Offshore Platforms (JTOP). The application of earthquake time series as the DL model input feature is not common in the fast seismic performance evaluation of structures. Additionally, in practical scenarios, the predictive performance of 1D-CNN is heavily reliant on the selection of appropriate model architecture and hyperparameters. Therefore, this study proposes a framework for systematically searching the neural architecture and hyperparameters of deep learning models, particularly 1D-CNN, through Bayesian optimization. Since manual search for model components can be a difficult task that may require numerous trial-and-error attempts or parametric studies, the aim of this framework is to streamline and organize the typical workflow of developing DL models to ensure optimal performance. Moreover, the comprehensive nested cross-validation method has been utilized to achieve an unbiased evaluation. The focus of this study is on the seismic collapse prediction of Jacket Type Offshore Platforms with 1D-CNN, and to fulfill this objective, a stochastic optimization algorithm is proposed to perform the stratified K-fold cross validation, specifically developed for the dataset preparation based on the incremental dynamic analysis (IDA). For comparison purposes, the presented framework is also applied for the hyperparameter tuning of Multilayer Perceptron (MLP) and Support Vector Machine (SVM) techniques. The effectiveness of the presented framework is evaluated for a case study JTOP structure subjected to an ensemble of earthquake records. The results demonstrate the capability of 1D-CNN model, developed using the proposed framework, for computing the collapse fragility curves of JTOPs.

Masonry compressive strength plays a crucial part in the response of historic and existing masonry buildings under both static and dynamic actions. Therefore, determining the compressive strength of masonry is essential for predicting the structural response of buildings to mechanical loading. Closed-form empirical expressions for predicting the compressive strength of masonry rely mostly on the compressive strength of the constituent materials and less often on geometric parameters. Such expressions are typically characterised by a narrow application spectrum and low accuracy. Artificial intelligence can be employed for clearly quantifying and mapping the influence of material and geometric parameters on the compressive strength of masonry. In this study an extensive and inclusive database of experimental results on the compressive strength of masonry was assembled. This database was used for training and validating a series of Back Propagation Neural Networks. Through rigorous statistical analysis of the predicted masonry compressive strength, the optimal machine learning architecture for the task was determined, leading to substantially more accurate predictions in comparison to all empirical expressions found in the literature. In turn, the architecture was employed for revealing and mapping the influence of the input parameters on the compressive strength of masonry, both individually and in combination, thus avoiding the “black box” nature of many studies in the area of complex artificial intelligence models.

Fluid viscous dampers (FVDs) have been widely used due to their capacity to generate dissipative forces (velocity dependent) that are not in phase with the displacements, namely able to exhibit their maximum forces when internal restoring forces are minimum. The possibility of increasing the damping ratio of a structure without significantly altering the inherent stiffness is another reason for the advantaging use of FVDs. For these characteristics, fluid viscous dampers are often preferred over other types of dampers. However, the lack of specific code prescriptions and simple but sufficiently reliable design procedures for structures exhibiting a non-linear plastic behavior is an issue not definitively faced. Deepening this issue could make the use of viscous dampers more diffused than it is. In this frame, here, a novel design procedure for non-linear FVDs to apply to hysteretic r.c. framed structures is proposed and discussed in terms of reliability in practical applications. The novelty of the procedure is that the scope of limiting the structural response is searched considering the contribution of external viscous damping, inherent viscous damping and hysteretic damping that the structure is able to exhibit. Therefore, the dimensioning of the external viscous dampers is carried out taking into account the rate of energy that the structure can dissipate by hysteretic damping differently from the most diffused approaches based on the maintaining of a structural elastic behavior. To this scope, the hypothesis of a simplified dynamic structural response is assumed to be coupled to the equivalent linearization of FVDs. The suitability of this hypothesis is discussed by a comparison between the obtainable results and the design targets in the case of structures that do not satisfy the assumed hypothesis. The results obtainable are analyzed in a statistical sense. Time history analyses of FVDs-equipped (and non) structural non-linear models are performed under appropriate families of base accelerograms. The design procedure is tested on benchmark models and on a case study in order to assess the degree of success of the proposed approach in connection to the assumed target objectives.

During natural disasters, such as earthquakes followed by tsunami events, bridges represent critical infrastructures whose failure can severely hinder emergency response and recovery efforts. Coastal regions are among the most susceptible to facing these unfortunate occurrences and it is also well established that even Mediterranean areas have experienced significant events of combined earthquake-tsunami actions. The 1908 Messina tsunami, triggered by a powerful earthquake, devastated southern Italy, causing many facilities and leaving a lasting memory to the Mediterranean regions’ inhabitants. The focus of this research is to provide insights useful for more resilient infrastructure design standards for multi-hazard scenarios and effective mitigation measures. A probabilistic multi-hazard fragility assessment framework is presented to evaluate the structural vulnerability of bridges subjected to sequential earthquake and tsunami loads, since a significant increase in vulnerability occurs when seismic damage precedes tsunami loading, underscoring the need for integrated design and assessment strategies. The proposed methodology employs Monte Carlo simulations to account for uncertainties in tsunami forces and structural configurations, providing a statistically robust tool for vulnerability assessment. Representative bridge typologies from Mediterranean coastal zones are analyzed through a two-step process: nonlinear time-history analyses simulate seismic action, then force-controlled pushover analyses model tsunami impact. The simulation results have been examined to identify the most appropriate analytical lognormal-distributions capable of accurately representing structural fragility. A comprehensive comparison between the numerical results and the derived fragility functions has been conducted, confirming the robustness and precision of the proposed methodology.

In recent years, the scientific community has shown a growing interest in studying the effects that tsunami loads may involve on structures, as these powerful natural forces impact coastal regions with a certain recurrence. Especially after the Great Indian Ocean Tsunami event in 2004, researchers have shown an increasingly aware that tsunami risk is a real and significant threat that must be taken into consideration, given the immense destruction and loss of life that it can cause, as has already been demonstrated in past events. Against this background, numerous experimental, numerical, and analytical studies have been addressed to this topic, driven by the need to understand the full range of consequences that can result from hydraulic tsunami actions on structures. The results gained from these studies are intended to provide valuable insights to assist engineers in designing safer structure and infrastructure in areas prone to such extreme loading. Nevertheless, despite the recent efforts, the mechanisms through which tsunami forces interact with structures and infrastructures remain not fully understood. As well as, the available technical documents are still incomplete, and the methodologies proposed continue to be a subject of debate.In the present study, some preliminary results of an experimental campaign conducted to investigate the response of a simplified two-storeys building model are presented and discussed. The experimental tests were carried out on a model, downscaled with a 1:25 ratio, subjected to tsunami wave action. These tsunami waves were generated in laboratory by a wavemaker inside a 2 × 2 × 40 m flow channel. In particular, these preliminary results refer to the high impact loading of focused waves on a scaled building model. The response of this building model was analyzed under varying inundation depths by measuring the time histories of forces. The results are discussed in detail and compared with the analytical formulations available in the literature.

The mitigation of seismic vulnerability in existing structures is a critical challenge in modern engineering. The implementation of energy dissipation devices has proven to be an effective solution for improving seismic resilience and reducing the risk. Numerous studies have demonstrated the effectiveness of damping systems in reducing excessive displacements and mitigating the impact of seismic forces on structural elements. In this frame, a novel approach to energy dissipation is discussed, based on the use of dampers able to exhibit variable friction depending on the external loading that here has been referred to as Variable Friction Dampers (VFDs). VFDs are able to modulate their dissipation capacity by combining a constant friction component with a variable damping component that increases as soon as the displacement increases, so an appropriate energy dissipation is provided based on the dynamic response of structures. The intrinsic characteristics of this innovative dissipation system in reducing displacements and mitigating seismic forces are numerically analyzed, with a focus on key design variables. For this purpose, a numerical model of the VFD system is developed, and a comparative seismic response analysis is carried out to evaluate its effectiveness under different seismic excitations. Considering the growing interest in developing effective strategies to reduce the impact of dynamic loads on strategic civil structures, applications for bridges are considered here.

The vulnerability of coastal buildings to tsunami events has garnered increasing attention in the last decades due to the severe impacts of recent disasters. Numerous challenges have been addressed to develop valuable methodologies to estimate the probability of damage to structures and infrastructures and to formulate effective strategies to mitigate potential damages and losses. However, a comprehensive predictive framework for constructing damage probability models applicable across different structural types and tsunami scenarios, while also accounting for the potential impact of previous earthquake damage, is still missing. In the present study, a double-stage approach for investigating the vulnerability of masonry buildings under sequential earthquake-tsunami events is proposed and discussed. Monte Carlo simulations are performed, analyzing the response of different building classes, characterized by different numbers of storeys. The random generation of the involved and basic parameters characterizing structures and loads have been followed by time-history dynamic analyses for seismic actions and push-over analyses for tsunami loading. The results provide valuable insights for refining multi-risk evaluations, highlighting the impact of earthquake-induced damage on tsunami vulnerability compared with studies that neglect seismic effects. In the paper, the influence of previous random seismic action on tsunami loading vulnerability was obtained and translated into a new type of fragility curve for the earthquake-tsunami interaction. Meanwhile, the important effect of building height on tsunami vulnerability is stressed along with the influence of vertical loads and seismic intensity, expressed in terms of Peak Ground Acceleration (PGA). Comparative analyses were conducted to assess the effectiveness of the proposed methodology for constructing analytical fragility curves, evidencing the reliability of the proposed approach.

The compressive strength of masonry walls constitutes a significant parameter that strongly influences the structural response of masonry buildings, under either static or dynamic actions. Significant variability is observed in the range of compressive strength values as highlighted by existing experimental investigations. Empirical relations providing the compressive strength also feature significant prediction divergence. This is attributed to large variations in the geometry and type of units, joint thicknesses, materials and building practices. Therefore, the need arises for the accurate prediction of the compressive strength of masonry walls, using data which is accumulated from past experiments. Artificial intelligence tools and machine learning techniques are considered in this study, to leverage the experience from those past experiments in predicting the compressive strength. A dataset of 611 specimens is developed, to the authors’ best knowledge comprises the largest dataset assembled for this purpose to date. Different Back Propagation Neural Networks models are trained and tested using the new dataset, leading to an optimal machine learning architecture. Results indicate that the optimal model can provide an improved prediction of the compressive strength as compared to literature proposals. Parameters which drastically affect the compressive strength are highlighted and expressions predicting the compressive strength are discussed.

The interest in building vulnerability to tsunamis has gained increasing attention within the scientific community. The destructive events in the last decades and the correlated losses in terms of human lives and things have been crucial in this growing interest. The issue of how constructions can contrast the tsunami waves or what influences their collapse has been different times faced when distinguishing between different building typologies. Here the parameter “building height” and its effect on the tsunami fragility of buildings are studied. Monte Carlo simulations are carried out for this purpose considering a range of tsunami scenarios and a set of buildings with random characteristics. Masonry buildings in the Mediterranean area are referred to whose diffusion overcomes any other structural typology. A simplified procedure is used for the assessment of buildings, based on the analyses of impacted walls at the ground floor in substitution of the analysis of the entire structure. This approach guarantees a reduced computational effort in favor of the possibility of managing a high number of cases (large-scale assessment) and obtaining of results statistically significant. This study wants to contribute to the enhancement of coastal resilience strategies by providing information about the relationship between masonry structure height and fragility under tsunami actions and providing valuable information to guide engineering practices, improve building codes and enhance the resilience of masonry structures against future tsunami events.

Pervious concrete, because of its high porosity, is a suitable material for reducing the effects of water precipitations and is primarily utilized in road pavements. In this study, the effects of binder-to-aggregate (B/A) ratios, as well as mineral admixtures with and without polypropylene fibers (PPFs) (0.2% by volume), including fly ash (FA) or silica fume (SF) (10% by substitution of cement), on the mechanical properties and durability of pervious concrete were experimentally observed. The experimental campaign included the following tests: permeability, porosity, compressive strength, splitting tensile strength, and flexural strength tests. The durability performance was evaluated by observing freeze–thaw cycles and abrasion resistance after 28 d curing. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermal analysis (TGA-DTA), and scanning electron microscopy (SEM) combined with energy dispersive spectroscopy (EDS) were employed to investigate the phase composition and microstructure. The results revealed that, for an assigned B/A ratio identified as optimal, the incorporation of mineral admixtures and fibers mutually compensated for their respective negative effects, resulting in the effective enhancement of both mechanical/microstructural characteristics and durability properties. In general, pervious concrete developed with fly ash or silica fume achieved higher compressive strength (>35 MPA) and permeability of 4 mm/s, whereas the binary combination of fly ash or silica fume with 0.2% PPFs yielded a flexural strength greater than 6 MPA and a permeability of 6 mm/s. Silica fume-based pervious concrete exhibited excellent performance in terms of freeze–thaw (F-T) cycling and abrasion resistance, followed by fiber-reinforced pervious concrete, except fly ash-based pervious concrete. Microstructural analysis showed that the inclusion of fly ash or silica fume reduced the harmful capillary pores and refined the pore enlargement caused by PPFs in the cement interface matrix through micro-filling and a pozzolanic reaction, leading to improved mechanical and durability characteristics of pervious concrete.

Plastic waste management has received significant attention in recent decades due to the urgent global environmental crisis caused by plastic pollution. The versatile and durable nature of plastic has led to its widespread usage across various sectors. However, its nonbiodegradable nature contributes to unsustainable production practices, leading to extensive landfill usage and posing threats to marine ecosystems and the food chain. To address these environmental concerns, numerous challenges have been recently addressed through investigating alternative approaches for disposing of plastic waste, with the construction sector emerging as a promising option. Incorporating plastic waste materials into concrete not only offers economic benefits but also provides a valid alternative to conventional disposal methods. This paper presents the results of different experimental studies, some of them available in the literature and others new, discussing the feasibility of integrating plastic waste into concrete and its impact on mechanical properties. The influence of different sizes, natures, treatments, and percentages of plastic waste in the concrete mixtures is dealt with in order to provide further data for helping to understand the nonunivocal results in the literature, under the conviction that only further observations can help to understand the mechanics of concrete with plastic aggregates. The experimental investigation highlighted that one parameter that is better than others and can be considered to compare different experimental investigations is the variation in weight (due to the effective volume of plastics in the mix), determining a sort of increasing of porosity that degrades the mechanical characteristics. However, this seems inconsistent in some cases. Therefore, the need for further research is highlighted to refine production methods and optimize mix designs.

The use of Fabric-Reinforced Cementitious Matrix (FRCM) systems is an innovative method for strengthening structures, particularly masonry, while addressing environmental and economic concerns. Despite their widespread use, characterizing FRCM composites poses challenges due to their complex mechanical behavior and considerable variability in properties. The available standardized testing methods exhibit some inconsistencies, underscoring the need for reliable characterization procedures. This paper presents an experimental study on the bond behavior between FRCM materials and calcarenite stone using a non-standard setup for double shear bond tests. Different FRCM systems are considered, varying the matrix composition and fabric nature. The experimental results are evaluated in terms of maximum stress, slip and data dispersion, alongside comparisons with double shear tests on larger samples and single-lap shear. These findings provide insights into how the mortar nature influences the stress-slip curves, strength, ductility and failure modes. The experimental study demonstrates the repeatability and robustness, particularly in terms of peak strength, of the non-standard setup configuration utilized in the study. The study highlights the importance of reliable characterization procedures for FRCM materials, especially in bond behavior assessments, emphasizing the need for further research to enhance our understanding of their application in structural reinforcement.

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.