A. Jiménez Rios, M.E.A. Ben Seghier, V. Plevris and J. Dai, "Explainable Ensemble Learning Framework for Estimating Corrosion Rate in Suspension Bridge Main Cables", Results in Engineering, 23, Article ID 102723, 15 pages (DOI: 10.1016/j.rineng.2024.102723), 2024.

Abstract:
Ensuring the safe operation of suspension bridges is paramount to prevent unwanted events that can cause failures. Therefore, it is crucial to continuously monitor their operational status to uphold safety and reliability levels. However, natural deterioration caused by the surrounding environment, primarily due to corrosion, inevitably impacts these structures over time, particularly the main cables made of steel. In this study, a robust framework is proposed to predict the annual corrosion rate in main cables of suspension bridges, while investigating the impact of the surrounding environmental factors on this process. To do so, the implementation of four regression models and four machine learning techniques are used in the first phase for modeling the annual corrosion rate based on a comprehensive database containing various environmental factors. The modeling performance is evaluated through a range of statistical and graphical metrics. After that, Shapley Additive Explanations (SHAP) is utilized to explain the model and to extract the impact of each variable on the final modeling results. Overall, the findings demonstrate the effectiveness of the proposed framework for addressing this issue. The Extreme Gradient Boosting (XGB) emerged as the top-performing model, achieving an overall R2 of 0.982. Moreover, the SHAP findings highlight the impact of CL− on the annual corrosion rate as the factor with the highest influence during the modeling process. The high performance of the proposed model suggests its potential utility in further research concerning the reliability of suspension bridge main cables.

Keywords:
Suspension bridges; Main cables; Annual corrosion rate; Ensemble learning models; Regression techniques; Shapley additive explanations.

A. Jiménez Rios, M.L. Petrou, R. Ramirez, V. Plevris, M. Nogal, "Industry 5.0, Towards an Enhanced Built Cultural Heritage Conservation Practice", Journal of Building Engineering, 96, 20 pages (DOI: 10.1016/j.jobe.2024.110542), 2024.

Abstract:
The rise of Industry 4.0 has led to a rapid increase in digitalization and industrial operations. However, it has recently been deemed insufficient in fulfilling European objectives for 2030. In response, and to counteract the unintended negative consequences triggered by Industry 4.0, Industry 5.0 has been introduced. The purpose of this article is to shed light on how the architecture, engineering, construction, management, operation, and conservation industry can adapt and better prepare to embrace novel Industry 5.0 principles and enabling technologies, ultimately resulting in enhanced conservation practices for the built cultural heritage environment. To achieve this, a systematic literature review was conducted following the PRISMA methodology. The principal results of this article highlight the work of different conservation professionals and our views on the potential of Industry 5.0 for enhancing conservation practices. Major conclusions indicate that artificial intelligence and digital twins are the two most studied technologies in the field. Sustainability is broadly discussed throughout the analyzed literature, whereas resilience and human centrism require further research and implementation efforts to achieve a holistic Industry 5.0 adoption. The significant scientific novelty of this work lies in the comprehensive scope of the review in terms of principles and enabling technologies, with a particular emphasis on heritage buildings. Thus, it is valuable for conservation practitioners seeking best practices, for policymakers as it suggests ways to encourage the adoption of novel technologies and principles in conservation, and for researchers as it highlights gaps and stimulates further paths of research and innovation.

Keywords:
Industry 5.0; Human-centrism; Resilience; Sustainability; Built cultural heritage environment; Conservation; Systematic literature review

V. Plevris, "AI-Driven Innovations in Earthquake Risk Mitigation: A Future-Focused Perspective", Geosciences, 14(9), 29 pages (DOI: 10.3390/geosciences14090244), 2024.

Abstract:
This study explores the transformative potential of artificial intelligence (AI) in revolutionizing earthquake risk mitigation across six key areas. Unlike traditional approaches, this paper examines how AI-driven innovations can uniquely enhance early warning systems, enabling real-time structural health monitoring, and providing dynamic, multi-hazard risk assessments that seamlessly integrate seismic data with other natural hazards such as tsunamis and landslides. It introduces groundbreaking applications of AI in earthquake-resilient design, where generative design algorithms and predictive analytics create structures that optimally balance safety, cost, and sustainability. The study also presents a novel discussion on the ethical implications of AI in this domain, stressing the critical need for transparency, accountability, and bias mitigation. Looking forward, the manuscript envisions the development of advanced AI platforms capable of delivering real-time, personalized risk assessments, immersive public training programs, and collaborative design tools that adapt to evolving seismic data. These innovations promise not only to significantly enhance current earthquake preparedness but also to pave the way toward a future where the societal impact of earthquakes is drastically reduced. This work underscores the potential of AI’s role in shaping a safer, more resilient future, emphasizing the importance of continued innovation, ethical governance, and collaborative efforts.

Keywords:
artificial intelligence (AI); earthquake risk mitigation; seismic hazard mapping; structural health monitoring; multi-hazard risk assessment; earthquake-resilient design; real-time data integration.