Smart Materials-Enabled Self-Healing Concrete for Sustainable Infrastructure Monitoring

Thesis Abstract

This study addresses the escalating maintenance costs and safety risks associated with aging concrete infrastructure by exploring smart materials-enabled self-healing concrete (SM-SHC) as a proactive remediation approach to sustain structural performance and monitoring capabilities. The aim is to evaluate the effectiveness of SM-SHC in real-world service conditions and to establish an integrated framework for automated health assessment and repair decision-making. Specific objectives include (1) to quantify the self-healing efficiency of microcapsule and bacteria-based healing agents under cyclic loading and environmental exposure; (2) to develop a digital twin model that links self-healing kinetics with crack evolution and corrosion indicators; (3) to assess the embedded sensing network performance for real-time monitoring of healing progression using piezoresistive, NFC-enabled, and fiber-optic sensors; (4) to evaluate the life-cycle environmental and economic benefits of SM-SHC relative to conventional concrete; and (5) to formulate design guidelines and an emergency maintenance protocol for civil infrastructure. The methodology adopts a mixed-methods embedded experimental-analytic design within a realist-constructivist epistemology to triangulate quantitative healing metrics with qualitative insights from engineering practitioners. The population comprises concrete test specimens and pilot-scale structural elements constructed with cementitious matrices incorporating microcapsule healing agents and bacterial spores. A stratified random sample of 120 standard cylindrical specimens and 12 reinforced concrete beam specimens is selected to represent varying crack widths (0.05–0.40 mm) and environmental conditions (wet-dry cycles, chloride exposure, and elevated temperature). Data collection employs (i) non-destructive tests including electrical impedance tomography (EIT), ultrasonic pulse velocity (UPV), and acoustic emissions to monitor crack evolution, (ii) high-resolution digital image correlation (DIC) for surface crack healing assessment, (iii) microstructural analysis using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) to characterize microcapsule and bacterial activity, and (iv) a digital twin implemented in a BIM-based platform integrating sensor data for real-time health monitoring. Validity and reliability are addressed through repeated measures, calibration against standard pull-off and flexural tests, and pilot studies to confirm sensor calibration. Ethical considerations adhere to industrial safety standards and data governance for collaborating agencies. Data analysis employs a hierarchical regression framework to quantify the contribution of healing efficacy, healing agent type, and environmental factors on crack closure rate and stiffness recovery, complemented by time-to-heal survival analysis. ANOVA is used to compare healing performance across agent configurations, while structural equation modeling (SEM) tests the relationships between healing indicators, sensor reliability, and structural health outcomes. Thematic analysis of practitioner feedback informs operational feasibility and informs the development of decision-support algorithms. Model specification includes a dynamic Bayesian network to capture probabilistic dependencies among crack growth, healing events, and sensor readings over time. Key expected findings include (i) quantifiable improvement in crack closure and stiffness recovery in SM-SHC specimens relative to control, with microcapsule-based systems achieving up to 65% crack width reduction under wet-dry cycles; (ii) sustained sensing accuracy of below 5% relative error for impedance-based crack detection and robust data fusion within the digital twin; (iii) demonstrable reductions in maintenance downtime and life-cycle cost of up to 25–40% under specified service life scenarios, and (iv) a validated design framework aligning material formulation, sensor network, and monitoring algorithms for scalable deployment in bridge decks and tunnel linings. The contribution to knowledge includes (i) a comprehensive empirical evaluation of SM-SHC under realistic service conditions, (ii) an integrated digital twin framework linking material self-healing kinetics with structural health monitoring, (iii) evidence-based guidelines for material formulation, sensor integration, and retrofit strategies, and (iv) a decision-support protocol for proactive maintenance and risk management. The study concludes that SM-SHC can substantially extend infrastructure service life while enabling continuous health monitoring, and recommends standardized performance metrics, field trials in diverse climate zones, and collaboration with standards bodies to codify design and retrofit practices.