Physics-Informed Decision Framework for Reuse of Reclaimed Steel Members Under Uncertainty

Structural steel reuse can gain large embodied-carbon savings, yet it is still not widely adopted since approval depends on the quality of the evidence, how uncertainty is handled, and if the design requirements are followed, not just on resistance. Reclaimed members frequently lack dependable documentation regarding material grade, loading history, boundary conditions, connection status, and degradation. For reuse decisions, conservative default assumptions protect safety but frequently eliminate qualified reuse options. This research examines data-driven and physics-informed computational methods from a decision-making standpoint, contending that their significance resides in facilitating an auditable approval process, not in supplanting deterministic verification. We differentiate feasibility, acceptability, and approval as distinct engineering phases. Data-driven models are thought of as tools for quickly screening candidates, surrogate evaluation, inverse reasoning, and stock-to-demand matching. Their goal is to reduce the list of candidates and prioritize evidence collection. Physics-informed approaches are examined as admissibility filters that impose restrictions of equilibrium, compatibility, stability, and plausible boundary-condition envelopes; therefore, minimizing mechanically invalid predictions under partial information. Next, we consider uncertainty quantification and explainability to be essential for reuse decisions. We suggest practical outputs for approval packages, such as resistance bounds within specified assumption envelopes, sensitivity rankings of decision-critical unknowns, low-support flags, and evidence actions for conditional acceptance. This document is organized into a process from audit to approval. It also states the open issues in reuse-specific datasets, standardized evidence capturing, decision-relevant validation under degradation, and regulatory acceptance. The resulting framework clarifies how advanced computational tools can enable adaptable, conservative, and transparent steel reuse in practice.