Selecting the optimal intensity measure (IM) is essential for accurately assessing the seismic performance of the submarine shield tunnels in the layered liquefiable seabed. However, current research relies on simplistic ranking or filtering methods that neglect the different contributions of each evaluation criterion on IM's overall performance. To address this, this study begins by developing a numerical simulation method for nonlinear dynamic analysis, considering joint deformation, ocean environmental loads, and soil liquefaction, which is validated by experimental and theoretical methods. Subsequently, a fuzzy multiple criteria decision-making (FMCDM) method based on fuzzy probabilistic seismic demand models (FPSDM) is proposed, which integrates the fuzzy analytical hierarchical process (FAHP) for calculating weights and the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) for ranking IM alternatives. Finally, tunnel damage is classified into four states employing joint opening as the index for measuring damage, then the seismic fragility analysis is conducted. The results indicate that the optimal IM of a submarine shield tunnel situated in layered liquefiable seabed is sustained maximum velocity (SMV). Furthermore, the comparison between the fragility curves established using SMV and peak ground acceleration (PGA) reveals PGA, a frequently employed IM, notably undervaluing the seismic hazard.

This paper has attempted to determine the weighting levels of the soil and ground motion parameters (engineering bedrock depth (EBd), average shear wave velocity (Vs30), fundamental frequency (f0), peak ground acceleration (PGA), Joyner-Boore distance (Rjb), and epicenter distance (Repi)) in reflecting the actual damage status after the 2023 Kahramanmara & scedil; earthquakes, which have a wide impact area of 11 provinces. The analytical hierarchy method (AHP), a multi-criteria decision-making (MCDM) process, was used to analyze these parameter data sets obtained from 44 Disaster and Emergency Management Presidency of T & uuml;rkiye (AFAD) stations (Gaziantep, Hatay, Kahramanmara & scedil;, and Osmaniye). The priority order of the parameters before the analysis was systematically collected. These parameters were categorized into soil, ground motion and earthquake source-path properties. Considering the literature, these characteristics and their combined effects were systematically weighted with AHP under five groups. According to the weighted groups in the scope of the study, the actual damage data can be determined with a minimum accuracy rate of 70% (Group 1). In comparison, the best performance evaluation was 82% (Group 5). The parameter order and weights in the actual damage data evaluation are suggested as EBd-%28, PGA-%24, Vs30-%19, Rjb-%14, f0-%10, and Repi-%5 considering the very high accuracy rate of Group 5. This suggested weighting allows the rapid and effective estimation of the damage distribution after a possible earthquake only with soil, ground motion and earthquake source-path characteristics, even in cases where reliable structure data cannot be obtained.

Uncontrolled wildfires pose a significant threat, potentially causing extensive damage to biodiversity, soil quality and human resources. It's crucial to swiftly detect and predict these wildfires to minimize their catastrophic consequences. To address this, our research introduces a wildfire prediction model that ranks cities based on risk leveraging multi-criteria decision-making (MCDM) to systematically assess conflicting factors in decision-making. This model integrates wildfire risks into a city's resilience strategy, utilizing fuzzy set theory to manage imprecise data and uncertainties. As part of this approach, we compile a new dataset encompassing weather patterns, vegetation types, terrain features and population density across various Californian cities. Ultimately, the model assesses and ranks the wildfire risk for each city in California.

Approximately 11% of the world's population lives within 10 km of an ocean coastline, a percentage that is likely to increase during the remainder of the 21st century due to urbanization and economic development. In the presence of climate change, coastal communities will be threatened by increasing damages due to sea-level rise (SLR), accompanied by hurricanes, storm surges and coastal inundation, shoreline erosion, and seawater intrusion into the soil. While the past decade has seen numerous proposals for coastal protection using adaptation methods to deal with the deep uncertainties associated with a changing climate, our review of the potential impact of SLR on the resilience of coastal communities reveals that these adaptation methods have not been informed by community resilience or recovery goals. Moreover, since SLR is likely to continue over the next century, periodic changes to these community goals may be necessary for public planning and risk mitigation. Finally, community policy development must be based on a quantitative risk-informed life-cycle basis to develop public support for the substantial public investments required. We propose potential research directions to identify effective adaptation methods based on the gaps identified in our review, culminating in a decision framework that is informed by community resilience goals and metrics and risk analysis over community infrastructure life cycles.