In the Architecture, Engineering, and Construction (AEC) industry, particularly civil engineering, the Finite Element Method (FEM) is a widely applied method for computational designs. In this regard, computational simulation has increasingly become challenging due to uncertain parameters, significantly affecting structural analysis and evaluation results, especially for composite and complex structures. Therefore, determining the exact computational parameters is crucial since the structures involve many components with different material properties, even removing some additional components affects the calculation results. This study presents a solution to increase the accuracy of the finite element (FE) model using a swarm intelligence-based approach called the particle swarm optimization (PSO) algorithm. The FE model is created based on the structure’s easily observable characteristics, in which uncertainty parameters are assumed empirically and will be updated via PSO using dynamic experimental results. The results show that the finite element model achieves high accuracy, significantly improved after updating (shown by the evaluation parameters presented in the article). In this way, a precise and reliable model can be applied to reliability analysis and structural design optimization tasks. During this research project, the FE model considering the PSO algorithm was integrated into an actual bridge’s structural health monitoring (SHM) system, which was the premise for creating the initial digital twin model for the advanced digital twinning technology.

Transportation infrastructures, especially roadway bridges, play a pivotal role in socioeconomic development. Recently, bridge engineers are increasingly facing the challenge in terms of shifting their strategy from building new facilities to maintaining the existing aging infrastructures, to preserve their service performance during the operational stage. In fact, the infrastructure administrators lack a quality control (QC) strategy for the existing roadway bridges, which leads to the decision-making application and tool being still minor. To overcome those challenging issues, this paper proposes a quality control framework for roadway bridge management using key performance indicators (KPIs). The case study methodology is suggested to be used and then conducted for several bridges, mostly in European countries. In which the performance indicators (PIs) and goals (PGs) are defined, after assessing the bridges and vulnerable zones, the derivation KPIs from those PIs are introduced and developed considering time functions and different maintenance scenarios. Eventually, a two-stage quality control framework will be proposed in which the static stage includes preparatory works, inspection responsibilities, and a quick assessment of KPIs; while the dynamic stage helps the decision maker in estimating the time remaining of the bridge service life, managing the evolution of KPIs as well as planning the best possible maintenance strategy. The selected two case studies are present and curated, which show the excellent potential to develop a long-term strategy for roadway bridge management on a lifecycle level.

Qualitative and quantitative assessments evaluate the structural vulnerability of liquid storage tanks. Liquid storage tanks are typically constructed and operated in areas with hard soils to minimize confining influences. However, many of these critical structures are in coastal areas with soft soils. The research conducted in this study entails the utilization of the finite element method accurately model the seismic behavior of a semi-buried concrete tank under various conditions, including changing water levels and soil properties. The study examines fluid–structure and soil–structure interactions through dynamic analyses of the rectangular semi-buried tank and comparing its different parameters. It also identifies sensitive areas where there is a probability of liquid leakage in storage tanks. The modeling is compared with the qualitative evaluation in the Japanese vibration capability diagnosis table. The results show that the tensile stress in the wall adjacent to the expansion joint is greater than the corresponding stress in the wall in all cases. In the dynamic analyses of the soil types, the pressure on the surface increases with increasing water height. A comparison of the quantitative and qualitative evaluation results shows the possible leakage of the tank in soft soil in the expansion joint.

Climate change is increasing the frequency and intensity of natural hazards, placing additional stress on critical infrastructure systems. Addressing these challenges requires both robust evaluation frameworks and the inclusion of Nature-Based Solutions (NbSs) alongside conventional protection measures. Building on the RAMSSHEEP concept, originally proposed for risk-driven maintenance, and later further developed and applied in, e.g., previous Horizon projects and COST Action TU1406, this study integrates natural hazard considerations and NbS risk mitigation measures into a comprehensive approach to evaluate the resilience of critical infrastructure. The novel methodology involves a structured expert elicitation process with participants from the Horizon NATURE-DEMO project, to adapt and extend the RAMSSHEEP framework for resilience-oriented transformation. This also includes alignment with established hazard and risk assessment systems to ensure methodological consistency and applicability of the final concept. The resulting framework enables systematic evaluation of infrastructure vulnerability and resilience, explicitly accounting for natural hazards and the contribution of NbSs to risk mitigation. The expected outcome is an objective, repeatable assessment methodology that supports decision-makers in planning, prioritizing, and monitoring resilience-enhancing measures across the infrastructure life cycle. A particular focus of this contribution lies in the methodological approach, ensuring its applicability within interdisciplinary and multi-level decision-making contexts.