Special Collections and Issues

Submit Paper » Modern engineering systems, from vibration-sensitive mechanical devices and safety-critical structures to aerospace, transportation, industrial, and energy applications, are increasingly equipped with advanced sensing, modeling, and control technologies to enhance safety, serviceability, and resilience. However, assessing and predicting their performance are inevitably affected by multiple sources of uncertainty, including variability in material and geometric properties, modeling simplifications, measurement noise, environmental effects, and the stochastic nature of external excitations. ...

Submit Paper » Description The rapid advancement of sensing technologies, structural health monitoring (SHM), and intelligent data analytics has significantly enhanced the ability to detect damage and diagnose faults in engineering systems. From bridges and tunnels to energy facilities and transportation networks, large volumes of monitoring data are continuously collected through distributed sensors, imaging systems, and inspection platforms. Meanwhile, advances in signal processing, machine learning, and deep learning have enabled increasingly accurate and automated condition assessment. ...

Submit Paper » Description The digital era is reshaping geotechnical uncertainty quantification (UQ) and reliability analysis through modern sensing, continuous monitoring, high-performance computing, and digital-twin ecosystems. Practice is increasingly data-rich, yet still challenged by sparse/biased data, nonstationarity, and complex ground–structure interactions. Meanwhile, the rapid integration of machine learning with physics-based and probabilistic models raises new demands for rigor, transparency, data efficiency, and deployability. This Special Collection seeks original research and practice-oriented advances that (i) separate, represent, propagate, and reduce uncertainty from site characterization and design to construction and operation, and (ii) translate uncertainty into decision-ready reliability and risk metrics. Contributions featuring verification/validation, uncertainty-aware interpretability, and reproducible workflows or well-documented datasets/case studies are particularly encouraged. ...

Submit Paper » Description This Special Collection (SC) aims to provide a dedicated space for the in-depth exploration and dissemination of advancements in reliability-based, risk-based, and uncertainty-informed decision-making. The primary goal of this SC is to showcase emerging developments that address reliability and risk management to enhance the resilience and sustainability of our infrastructure systems and built environment. Contributions are expected to present key ideas, concepts, and technologies for solving significant challenges posed by the high complexity and multidisciplinary nature of problems as well as the comprehensive quantification, efficient processing, and management of induced uncertainties. By establishing this SC, we aim to foster a collaborative environment that encourages researchers to share insights and innovations in the multifaceted fields of risk, uncertainty, and decision-making within infrastructure systems. ...

Submit Paper » About this joint special collection This multi-journal ASCE special collection brings together research, applied case studies, and forward-looking perspectives on engineering solutions for climate adaptation and infrastructure resilience. Topics include risk modeling, performance-based and life-cycle engineering, adaptive design, resilient infrastructure planning, digital twins, artificial intelligence, and decision-making under deep uncertainty. ...

Submit Paper » Description Reliability is a key aspect of safety-critical structures and systems such as bridges, aircrafts, dams, and nuclear structural facilities. For this reason, the performance of such structures and systems needs to be assessed via reliability analysis, to ensure they operate safely and, in turn, protect lives. ...

Submissions for this archived call are closed. Description The emergence of large language models (LLMs) and multimodal foundation models has revolutionized traditional approaches to risk assessment and predictive maintenance in engineering systems. These AI systems demonstrate unprecedented capabilities in processing heterogeneous data streams - from textual maintenance logs and equipment manuals to time-series sensor data and visual inspection reports - enabling comprehensive fault diagnosis across civil infrastructure (e.g., bridges, dams, power grids) and mechanical systems (e.g., rotating machinery, HVAC systems, industrial robots). However, the deployment of LLMs in safety-critical engineering applications introduces profound challenges that demand urgent research attention. First, the probabilistic nature of LLMs leads to inherent epistemic uncertainty in fault diagnosis, compounded by issues of model hallucination when interpreting sparse or noisy field data. Second, the black-box decision-making process of current models creates significant barriers to engineering validation, particularly in regulated industries where traceable risk assessment is mandatory. Third, the dynamic operating conditions of engineering systems (e.g., seasonal load variations in civil infrastructure or wear progression in mechanical components) require continuous model adaptation while maintaining operational safety margins. This special issue seeks to address these challenges through cutting-edge research at the intersection of AI reliability and engineering risk management. ...

Submissions for this archived call are closed. The rapid evolution of mechanical systems and increasing industrial complexity have driven the need for advanced predictive maintenance strategies. Cognitive Digital Twins (CDTs), integrating AI, real-time data analytics, and cognitive computing, have emerged as a transformative solution. Unlike traditional digital twins, CDTs can learn, reason, and adapt, enabling more accurate and dynamic predictive maintenance. However, their reliability is challenged by modeling uncertainties, sensor noise, environmental variability, and unforeseen operational conditions. ...

Submissions for this archived call are closed. Industrial equipment, such as engine, robot, machine tool, energy harvester, vehicle, etc., plays a pivotal role in enhancing production efficiency, ensuring product quality, and reducing labor expenses. However, the randomness of structural parameters and external excitations can potentially threaten the operation and safety of industrial equipment. Consequently, structural reliability, which can quantify the given performance and safety level of system under various uncertainties, is essential to ensure the quality of industrial equipment. ...

Submissions for this archived call are closed. Background Bayesian inference provides a fundamental probabilistic framework to quantify uncertainty, incorporate evolving information, and make informed decisions. Over the years it has attracted ever-growing interest in various fields of science and engineering. In structural health monitoring (SHM) the approach has been explored for addressing challenges in extracting actionable information from data for structural identification, load estimation, damage diagnosis and prognosis, and remaining useful life prediction, for unknown and potentially changing structure and environment. Amidst emerging technologies such as artificial intelligence, machine learning, and digital twin, there are opportunities for exploring Bayesian techniques along deep learning methods to better account for modeling errors and uncertainties. This special collection aims to create a collaborative research platform for academics and practitioners worldwide to present the latest advances in Bayesian inference for SHM, focusing on its applications in monitoring and decision-making, covering the targeted engineering systems of civil infrastructure, mechanical systems, and aerospace structures. ...