A Chemistry-Informed Non-Linear Ensemble and Enterprise Risk Optimization Framework for Structural Infrastructure Asset Management: Overcoming Data Anisotropy in Sustainable Material Engineering

Conventional enterprise asset management models within municipal and industrial infrastructure fail to structurally align physical asset degradation kinetics with high-level corporate risk governance. This operational mismatch stems from two key issues: the localized, non- linear structural behavior of materials under varied environmental conditions, and the data silos that isolate raw laboratory telemetry from overarching Enterprise Risk Management (ERM) dashboards. This study addresses this gap by presenting a unified, chemistry-informed non-linear ensemble computing framework that scales from component-level mechanical performance to institutional asset risk evaluation. On the physical material layer, the model integrates Non-Destructive Testing (NDT) pulse telemetry with an artificial neural network optimized via Gaussian Noise Augmentation (GNA) to map structural anisotropy and predict concrete compressive strength across ages from 3 to 180 days (21 MPa to 42 MPa). On the institutional layer, these material alerts feed directly into a cloud-integrated Big Data Business Intelligence (BI) engine structured under a formalized corporate governance protocol. Tested against an empirical validation timeline of 18 months using heterogeneous structural data streams, the framework demonstrates an improvement in asset lifecycle tracking accuracy over traditional linear regression matrices, while cutting institutional asset management overhead by establishing auditable, SHAP-driven policy metrics for capital recovery scheduling.