The Impact of Artificial Intelligence on Smart Infrastructure Development

The physical infrastructure, including bridges, roads, water and wastewater facilities, power transmission lines, and public buildings, is the basis of the built environment, although its assessment and management is largely based on periodic inspection intervals and visual inspection practices that were developed long before real-time structural monitoring became technically possible. This paper proposes an AI-Integrated Smart Infrastructure Framework (AISIF) combining a structural health monitoring and sensor fusion engine, a predictive maintenance and asset lifecycle optimisation module, and a resource allocation and infrastructure risk governance gateway, evaluated across 48 infrastructure authorities (transportation agencies$\mathrm{n}=\text{1 8}$, water and utility authorities$\mathrm{n}=\text{1 6}$, public works departments$\mathrm{n}=\text{1 4})$processing 1,684,000 infrastructure asset records (1,178,800 training / 505,200 test, 70/30 stratified split) and 62,400 monitored infrastructure assets across 6 asset categories (bridges, roads and pavement, water and wastewater, power transmission, public buildings, tunnels and underground infrastructure) over a 40-month evaluation horizon. The AISIF achieves a composite infrastructure management effectiveness score of$\mathrm{r}=\text{0. 8 9 1}$against expert-validated engineering assessment benchmarks, outperforming ML-only infrastructure analytics$(\mathrm{r}=\text{0. 7 4 2}$; +20.1% [(0.891-0.742)/0.742]), traditional fixed-schedule inspection systems$(\mathrm{r}=\text{0. 5 6 4}; +\text{5 8. 0 \%}[(\text{0. 8 9 1 - 0. 5 6 4}) / 0.564])$, and manual visual assessment methods$(\mathrm{r}=\text{0. 4 7 2}$; +88.8% [(0.891-0.472)/0.472]). Deployment outcomes: +44.8% structural defect detection accuracy, +56.2% predictive maintenance scheduling precision, -33.6% infrastructure failure incidents, +38.4% resource allocation efficiency, 91.8% regulatory compliance rate, and +26.4% asset lifecycle extension. Sub-domain performance: Structural Health Monitoring$(\mathrm{r}=\text{0. 9 3 6})$, Predictive Maintenance Scheduling (0.928), Resource Allocation Optimisation (0.898), and Infrastructure Risk Management (0.868). Legacy system integration constraints degrade AISIF accuracy to 0.858 (-3.7% [(0.891-0.858)/0.891]), and extreme weather event unpredictability to 0.841 (-5.6% [(0.891-0.841)/0.891]), with deployed mitigation protocols for both conditions.

Hali tarjima qilinmagan