Digital Intelligent System for Novel Construction Materials in Smart Infrastructure

Abstract

The construction of smart infrastructure has witnessed a rapid consumption of the intelligent construction materials which are expected to have better mechanical properties, higher durability, greater environmental friendliness and sustainable structure performance over time. Conventional methods for selecting and assessing construction materials rely largely on delayed laboratory experiments and retrospective engineering practices, leading to impractical construction costs and time expenses and a lack of adaptability to diverse environmental factors. And, many current construction material assessment methods lack real-time performance monitoring, predictive durability analysis as well as intelligent decision support for choosing the optimal material under engineered load and climatic conditions. Considering these deficiencies, this paper develops a Digital Intelligent Structure for Advanced Construction Materials for smart infrastructure, integrating Artificial Intelligence (AI), Machine Learning (ML), Digital Twin technology, IoT-sensor-based telemetry, BIM and analytics with cloud computing into a single digital platform for intelligent characterization and lifecycle assessment of next-generation materials such as self-healing concrete, ultra-high-performance concrete (UHPC), geopolymers and fibre-reinforced cementitious composites. Through continuous collecting lifestyle information of the proposed materials from sensors embedded in structural components as well as from specimen tests, the built-in ML algorithms analyze the multidimensional data sets to infer and forecast the compressive strength, crack propagation durability thermal performance, corrosion potential and lifecycle of the innovative materials, while the built-in Digital Twin continually models the physical properties in real-time against virtual visualizations for data-driven simulation of material behaviour and guides the decision models to recommend optimal construction material for structural durability, lifecycle cost and sustainability based on the structural design parameters, environmental conditions and accelerative computational results. The experimental results show that the proposed setup accurate classifies the advanced construction materials at 97.2%, predict the compressive strength at 96.4% and projects the durability at 95.8%, reduces material assessment time by 33.7%, enhances lifecycle performance estimation by 28.6% and increases decision accuracy by 30.8%, in comparison with traditional construction material assessment methods. The presented setup establishes a scalable and intelligent digital ecosystem for Construction 4.0 and Industry 5.0 in next-generation smart infrastructure by facilitating data-driven material optimization, predictive structural evaluation and sustainable construction, and will act as an effective platform for resilient and adaptable civil infrastructures.