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Over the past few decades, several base isolation systems have been developed to enhance the performance of structures under extreme earthquake shaking intensities. Recently, to achieve high energy dissipation capabilities, a new generation of multi-stage friction pendulum (FP) bearings known as the “Quintuple Friction Pendulum (QFP)” was introduced in the literature. With the help of its five effective pendula and nine operational regimes, this bearing's major benefits stem from its ability to accomplish complicated multi-stage adaptive behavior with smoothed loading and unloading when subjected to lateral forces.

Within the assessment context, five finite element models of reinforced concrete frames supported on QFP isolators with different properties will be developed in OpenSees. Thereafter, a set of 60 earthquakes will be analyzed using the nonlinear time history analysis approach, and the impact of each ground motion record's properties will be evaluated.

Overall, the study's findings have demonstrated that the characteristics of the isolator, combined with the type of earthquake being applied, have a substantial impact on the isolator's behavior.

Currently, no studies have examined the energy distribution of structural systems equipped with this type of isolation system while considering the influence of earthquake characteristics. Thus, this study is intended to extend the findings available in the literature by discussing and illustrating the distribution of strong ground motions input energy into highly nonlinear base-isolated systems that account for the bearing and superstructural materials' nonlinearity, geometric nonlinearity and leakage-prevented viscous damping nonlinearity. Besides, it investigates the influence of various earthquake characteristics on the energy dissipation of such buildings.

Earthquakes pose a significant challenge to human safety and the durability of infrastructure, highlighting the urgent need for innovative disaster management strategies. This study addresses the gap in current earthquake disaster management approaches, which are often related to issues of transparency, centralization and sluggish response times. By exploring the integration of blockchain technology into seismic hazard management, the purpose of the research is to overcome these limitations by offering a novel framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities.

This study develops an innovative approach to address these issues by introducing a blockchain-based seismic monitoring and automated decision support system for earthquake disaster management in smart cities. This research aims to capitalize on the benefits of blockchain technology, specifically its real-time data accessibility, decentralization and automation capabilities, to enhance earthquake disaster management. The methodology employed integrates seismic monitoring data into a blockchain framework, ensuring accurate, reliable and comprehensive information. Additionally, smart contracts are utilized to handle decision-making and enable rapid responses during earthquake disasters, offering an effective alternative to traditional approaches.

The study results highlight the system’s potential to foster reliability, decentralization and efficiency in earthquake disaster management, promoting enhanced collaboration among stakeholders and facilitating swift actions to minimize human and capital loss. This research lays the foundation for further exploration of blockchain technology’s practical applications in other disaster management contexts and its potential to transform traditional practices.

Current methodologies, while contributing to the reduction of earthquake-related impacts, are often hindered by limitations such as lack of transparency, centralization and slow response times. In contrast, the adoption of blockchain technology can address these challenges and offer benefits over various aspects, including decentralized control, improved security, real-time data accessibility and enhanced inter-organizational collaboration.

Currently, many experimental studies on the properties and behavior of rubberized concrete are available in the literature. These findings have motivated scholars to propose models for estimating some properties of rubberized concrete using traditional and advanced techniques. However, with the advancement of computational techniques and new estimation models, selecting a model that best estimates concrete's property is becoming challenging.

In this study, over 1,000 different experimental findings were obtained from the literature and used to investigate the capabilities of ten different machine learning algorithms in modeling the hardened density, compressive, splitting tensile, and flexural strengths, static and dynamic moduli, and damping ratio of rubberized concrete through adopting three different prediction approaches with respect to the inputs of the model.

In general, the study's findings have shown that XGBoosting and FFBP models result in the best performances compared to other techniques.

Previous studies have focused on the compressive strength of rubberized concrete as the main parameter to be estimated and rarely went into other characteristics of the material. In this study, the capabilities of different machine learning algorithms in predicting the properties of rubberized concrete were investigated and compared. Additionally, most of the studies adopted the direct estimation approach in which the concrete constituent materials are used as inputs to the prediction model. In contrast, this study evaluates three different prediction approaches based on the input parameters used, referred to as direct, generalized, and nondestructive methods.

This study aims to explore whether an auditee’s audit quality influences its payout policies (i.e. each form of dividend payouts and stock repurchase payouts).

Based on a panel data of US public firms, from 2004 to 2018, and Tobit estimators, this study aims to examine whether auditees’ audit quality is related to their payouts and under which circumstances (from the standpoints of auditees’ information asymmetry, refinancing risk, corporate governance and financial constraints) the aforesaid associations are more pronounced.

The findings of this study imply that auditees’ audit quality is positively related to auditees’ payouts. Further examination suggests that this positive relationship is stronger for auditees with higher information asymmetry, lower financial constraints and refinancing risk and for those with weaker governance. Finally, this study documents that dividend payouts are more stable for auditees with high-quality audits than those with low-quality audits. The results support the view that auditees’ transparency (reflected in high-quality audits) could be a crucial driver and rationale for their payout policies and, ultimately, overall policies.

By combining two different research lines of audit quality and corporate payout policies, this paper adds to both literature, as it is a novel one to document the contributing function and impact of audit quality on auditee’s payout policies (tangible financial decisions and policies). The findings are significant considering that it documents high-quality audits affecting the auditees besides their financial reporting quality. This study also shows the moderating roles of the auditee’s information asymmetry, rollover risk, financial constraints and corporate governance in the relation between audit quality and an auditee’s payout decisions. Furthermore, the findings can help shareholders (aiding them in determining companies with high payout policies), regulators and policymakers who emphasize audit quality. The results indicate that policymakers’ and standard setters’ efforts fostering high-quality audits should be in conjunction with firm payout standards.