Integration of Structural Health Monitoring with Sustainability Indicators: A Systematic Review Bridging Resilience and Green Construction Goals

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This review offers a comprehensive framework for linking SHM data with sustainability metrics, guiding future research on data-driven infrastructure management.

🏢実務担当者:Engineering firms can use the proposed framework to integrate SHM with sustainability reporting and optimize asset life-cycle costs.

🏛政策担当者:Regulators can consider standardizing metrics for infrastructure sustainability incorporating SHM data to support resilience and decarbonization policies.

The increasing demand for resilient and environmentally responsible infrastructure has driven a shift from conventional structural safety assessment toward integrated, performance-driven sustainability management. Structural Health Monitoring (SHM), traditionally used for damage detection and condition assessment, is increasingly recognized as a key enabler of sustainable infrastructure by providing continuous, data-driven insights across the asset life cycle. This systematic review synthesizes peer-reviewed literature published between 2000 and 2025 using a PRISMA-based methodology to examine how SHM can be integrated with sustainability indicators to bridge resilience objectives and green construction goals. The review traces the evolution of SHM technologies, sensor systems, and data analytics, highlighting their growing role in performance assessment, predictive maintenance, and post-event resilience evaluation for bridges, buildings, and heritage structures. It further examines sustainability indicators relevant to infrastructure systems, including energy performance, embodied carbon, material efficiency, durability, adaptability, and life-cycle impacts, emphasizing their importance in long-term decision-making and asset management. Through thematic synthesis, the review identifies emerging approaches that link SHM data with sustainability outcomes via digital tools such as BIM, digital twins, IoT-enabled monitoring, and machine learning–based predictive models. Persistent challenges are identified, including fragmented research, lack of standardized metrics, data interoperability issues, and barriers to cross-disciplinary adoption. To address these gaps, a conceptual framework is proposed that maps SHM outputs to sustainability indicators through feedback loops supporting adaptive management, life-cycle optimization, and resilience-informed sustainability strategies. Overall, the review establishes a foundation for advancing SHM as a core component of sustainable and resilient infrastructure systems.

• semanticscholar https://doi.org/10.64615/fjes...2026.109first seen 2026-06-29 07:31:42