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Structural health monitoring (SHM) is the most direct and advanced method for understanding the evolution laws of structures and ensuring structural safety. The essence of SHM lies in diagnosing structural health by analyzing monitoring data. Since the introduction of machine learning paradigm for SHM, using machine learning methods to analyze the monitoring data, identify, and evaluate structural health status has become a prominent research topic in this field. For complex bridge structures, diagnosing structural health based on highly incomplete monitoring data presents an inherent high-dimensional problem. Machine learning methods are particularly well-suited for addressing these issues due to their capabilities in effective feature extraction, efficient optimization, and robust scalability. This article provides a brief review of the developments in machine learning-based structural health diagnosis, including data cleaning, structural modal parameters estimation, structural damage identification, digital twin technology, and structural reliability assessment. Additionally, the paper discusses related open questions and potential directions for future research.

Reliability analysis of aeroengine structures is a critical task in aerospace engineering, but traditional methods often face challenges of low computational efficiency and insufficient accuracy when dealing with complex, high-dimensional, and nonlinear problems. This paper proposes a novel reliability assessment method (AC-Kriging) based on the Actor–Critic network and Kriging surrogate models to address these issues. The Actor network optimizes the sampling strategy for design variables, making sampling more efficient. The Critic network assesses the reliability of these samples to ensure accurate results, while a Kriging surrogate model replaces expensive finite element simulations and cuts computational cost. Three case studies demonstrate that AC-Kriging significantly outperforms traditional methods in both sampling efficiency and reliability estimation accuracy. This research provides an efficient and reliable solution for reliability analysis of aeroengine structures, with important theoretical and engineering application value. Three case studies demonstrate that AC-Kriging significantly outperforms traditional methods in both sampling efficiency and reliability-estimation accuracy, requiring only 52–147 samples to achieve comparable accuracy while maintaining the relative failure probability error within 0.87–7.27%. This research provides an efficient and reliable solution for the reliability analysis of aeroengine structures.

This study presents an AI-based framework that unifies civil and mechanical engineering principles to optimize the structural performance of steel frameworks. Unlike traditional methods that analyze material behavior, load-bearing capacity, and dynamic response separately, the proposed model integrates these factors into a single hybrid feature space combining material properties, geometric descriptors, and load-response characteristics. A deep learning model enhanced with physics-informed reliability constraints is developed to predict both safety states and optimal design configurations. Using AISC steel datasets and experimental records, the framework achieves 99.91% accuracy in distinguishing safe from unsafe designs, with mean absolute errors below 0.05 and percentage errors under 2% for reliability and load-bearing predictions. The system also demonstrates high computational efficiency, achieving inference latency below 3 ms, which supports real-time deployment in design and monitoring environments. the proposed framework provides a scalable, interpretable, and code-compliant approach for optimizing steel structures, advancing data-driven reliability assessment in both civil and mechanical engineering.

The structural integrity of offshore platforms is affected by degradation issues such as subsidence. Subsidence involves large settlement areas, and it is one of the phenomena that may be experienced by offshore platforms throughout their lives. Compaction of the reservoir is caused by pressure reduction, which results in vertical movement of soils from the reservoir to the mud line. The impact of subsidence on platforms will lead to a gradually reduced wave crest to deck air gap (insufficient air gap) and cause wave-in-deck. The wave-in-deck load can cause significant damage to deck structures, and it may cause the collapse of the entire platform. This study aims to investigate the impact of wave-in-deck load on structure response for fixed offshore structure. The conventional run of pushover analysis only considers the 100-year design crest height for the ultimate collapse. The wave height at collapse is calculated using a limit state equation for the probabilistic model that may give a different result. It is crucial to ensure that the reserve strength ratio (RSR) is not overly estimated, hence giving a false impression of the value. This study is performed to quantify the wave-in-deck load effects based on the revised RSR. As part of the analysis, the Ultimate Strength for Offshore Structures (USFOS) software and wave-in-deck calculation recommended by the International Organization for Standardization (ISO) as practised in the industry is adopted to complete the study. As expected, the new revised RSR with the inclusion of wave-in-deck load is lower and, hence, increases the probability of failure (POF) of the platform. The accuracy and effectiveness of this method will assist the industry, especially operators, for decision making and, more specifically, in outlining the action items as part of their business risk management.

The evaluation and prediction of reliability and testability of mining machinery and equipment are crucial, as advancements in mining technology have increased the importance of ensuring the safety of both the technological process and human life. This study focuses on developing a reliability model to analyze the controllability of mining equipment. The model, which examines the reliability of a mine cargo-passenger hoist, utilizes statistical methods to assess failures and diagnostic controlled parameters. It is represented as a transition graph and is supported by a system of equations. This model enables the estimation of the reliability of equipment components and the equipment as a whole through a diagnostic system designed for monitoring and controlling mining equipment. A mathematical and logical model is proposed to calculate availability and downtime coefficients for different structures within the mining equipment system. This analysis considers the probability of failure-free operation of the lifting unit based on the structural scheme, with additional redundancy for elements with lower reliability. The availability factor of the equipment for monitoring and controlling the mine hoisting plant is studied for various placements of diagnostic systems. Additionally, a logistic concept is introduced for organizing preventive maintenance systems and reducing equipment recovery time by optimizing spare parts, integrating them into strategies aimed at enhancing the reliability of mine hoisting plants.

\"Provides a comprehensive introduction to importance measures in reliability and optimization, allowing readers to address real, large-scale problems within various fields effectivelyThe book is divided into five main parts, the first containing background information on the fundamentals of system reliability. The second part introduces importance measures, including: the Birnbaum importance measure; the Barlow-Proschan importance measure; the Fussell-Vesely importance measure; and the Natvig time-dependent lifetime importance measure. This part also covers structure importance measures, importance measures of pairs of components, and generalizations of importance measures. Part three looks at applications. Importance measures in redundancy allocation and fault diagnosis are discussed, along with importance measures in upgrading systems and in operations research. The fourth part covers comparisons of importance measures and importance measures for con/k/n systems. The final part to the book discusses components assignment problems (CAP), including sections on CAP in coherent systems, CAP in con/k/n/ and its variant systems, and heuristics based on the Birnbaum reliability importance for CAP. A full appendix contains acronyms and notation, errors and ambiguities found in the literature. First book to systematically interpret various importance measures in the field of reliability engineering, to investigate the precise relationships among various importance measures, and to present their applications in the areas of reliability, operations research, and optimization Includes extensive coverage, from the early study of reliability importance to the state-of-the-art in network analysis, multistate systems, and applications in modern systems Contains many case studies, examples, illustrations and tables\"--

This paper addresses key challenges in the static and dynamic reliability analysis of engineering structures, particularly the difficulty in accurately estimating large reliability indices and small failure probabilities. For static reliability problems, a dual power transformation is employed to transform the performance function into a form approaching a normal distribution. The high-order unscented transformation is then applied to compute the first four moments of the transformed performance function. Subsequently, the fourth-moment method is used to calculate large reliability indices, offering a novel improvement over traditional methods such as FORM and SORM. For dynamic reliability problems, the low-discrepancy sampling technique is integrated to efficiently compute structural responses under random seismic excitation, improving computational efficiency for complex dynamic systems. The Yeo–Johnson transformation is introduced to normalize the extreme values of dynamic responses, and the first four moments of the transformed extreme values are statistically evaluated. Additionally, a third-order polynomial transformation (TPT) is applied to approximate the probability density function, leading to the derivation of the probability of exceedance (POE) curve. The optimal transformation parameters for both the dual power and Yeo–Johnson transformations are determined using the Jarque–Bera (JB) test. Four numerical examples, coupled with Monte Carlo simulation, validate the proposed framework’s accuracy and efficiency, providing a robust tool for static and dynamic reliability analysis. This unified approach represents a significant advancement by integrating novel transformations and fourth-moment method, providing a powerful and efficient tool for static and dynamic reliability analysis of engineering structures.

Aims: Child maltreatment (CM) is global public health issue with devastating lifelong consequences. Global organizations have endeavored to eliminate CM; however, there is lack of consensus on what instruments are most suitable for the investigation and prevention of CM. This systematic review aimed to appraise the psychometric properties (other than content validity) of all current parent- or caregiver-reported CM instruments and recommend the most suitable for use. Method: A systematic search of the CINAHL, Embase, ERIC, PsycINFO, PubMed, and Sociological Abstracts databases was performed. The evaluation of psychometric properties was conducted according to the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines for systematic reviews of patient-report outcome measures. Responsiveness was beyond the scope of this systematic review, and content validity has been reported on in a companion paper (Part 1). Only instruments developed and published in English were included. Results: Twenty-five studies reported on selected psychometric properties of 15 identified instruments. The methodological quality of the studies was overall adequate. The psychometric properties of the instruments were generally indeterminate or not reported due to incomplete or missing psychometric data; high-quality evidence on the psychometric properties was limited. Conclusions: No instruments could be recommended as most suitable for use in clinic and research. Nine instruments were identified as promising based on current psychometric data but would need further psychometric evidence for them to be recommended.

Resilience engineering depends on four abilities: the ability a) to respond to what happens, b) to monitor critical developments, c) to anticipate future threats and opportunities, and d) to learn from past experience - successes as well as failures. They provide a structured way of analysing problems and proposing practical solutions. This book is divided into four sections which describe issues relating to each of the four abilities. The section's chapters emphasise practical ways of engineering resilience, featuring case studies and real applications.

The current work presents a case study for the state-of-the-art multimodal risk assessment approach, which is especially appropriate for environmental wind-wave dynamic systems that are either directly physically observed or numerically modeled. High dimensionality of the wind-wave environmental system and cross-correlations between its primary dimensions or components make it quite challenging for existing reliability methods. The primary goal of this investigation has been the application of a novel multivariate hazard assessment methodology to a combined windspeed and correlated wave-height unfiltered/raw dataset, which was recorded in 2024 by in situ NOAA buoy located southeast offshore of Greenland. Existing hazard/risk assessment methods are mostly limited to univariate or at most bivariate dynamic systems. It is well known that the interaction of windspeeds and corresponding wave heights results in a multimodal, nonstationary, and nonlinear dynamic environmental system with cross-correlated components. Alleged global warming may represent additional factor/covariate, affecting ocean windspeeds and related wave heights dynamics. Accurate hazard/risk assessment of in situ environmental systems is necessary for naval, marine, and offshore structures that operate within particular offshore/ocean zones of interest, susceptible to nonstationary ocean weather conditions. Benchmarking of the novel spatiotemporal multivariate reliability approach, which may efficiently extract relevant information from the underlying in situ field dataset, has been the primary objective of the current work. The proposed multimodal hazard/risk evaluation methodology presented in this study may assist designers and engineers to effectively assess in situ environmental and structural risks for multimodal, nonstationary, nonlinear ocean-driven wind-wave-related environmental/structural systems. The key result of the presented case study lies within the demonstration of the methodological superiority, compared to a popular bivariate copula reliability approach.