Development of a Smart Concrete for Sustainable Flood-Resilient Urban Infrastructure: Design, Implementation, Evaluation

Thesis Abstract

Urban flooding, driven by climate variability and rapid urbanization, imposes substantial damages to life, property, and infrastructure, with conventional concrete systems often failing to adapt to dynamic hydrological loads. This study addresses the gap by developing a smart concrete that integrates sensing and self-regulating capabilities to enhance flood resilience, durability, and sustainability of urban infrastructure. The aim is to design, implement, and evaluate a smart concrete mix that (i) detects water ingress and moisture levels in real time, (ii) autonomously modulates porosity-driven drainage and self-healing mechanisms under flood loading, and (iii) demonstrates performance within a pilot urban roadway and a parameterized flood-retention wall. Specific objectives include (1) formulating a smart concrete mixture with embedded micro-sensors, phase-change materials, and superabsorbent polymers; (2) evaluating mechanical performance, permeability, and durability under accelerated hydro-mechanical conditioning; (3) developing a low-power, wireless sensing network and edge-computing capability for real-time monitoring; (4) calibrating a physics-informed, data-driven model to predict flood response and service life; (5) validating life-cycle implications and cost-effectiveness for urban deployment; and (6) delivering design guidelines for scalable implementation. The study adopts a mixed-methods, design-to-evaluate approach, underpinned by the resilience and smart-infrastructure theories, notably the resilience theory of urban systems and the tangible interaction framework for smart materials. The research design comprises laboratory experimentation, numerical simulation, and field validation. The population includes concrete mix designs, sensor technologies, and urban infrastructure components, with a purposive sampling of ten candidate smart mixes and eight sensor configurations subjected to a suite of hydro-mechanical tests. A total of 60 prism specimens (100 mm × 100 mm × 400 mm) and 20 cylinder specimens (150 mm diameter × 300 mm height) will be produced and tested, alongside three pilot installations in a metropolitan flood-prone district. Data collection uses multi-sensor data acquisition (voltage, impedance, moisture, strain), high-speed camera documentation, and periodic non-destructive evaluation (ultrasonic pulse velocity, surface resistivity). The study employs descriptive statistics, multivariate regression, and time-series analysis to quantify correlations between sensor signals and physical damage, along with ANOVA to compare performance across mixes. A Bayesian updating framework will integrate field data into the predictive model, while machine learning techniques (random forest and gradient boosting) will enhance flood-state classification. The analytical framework includes a finite-element model for structural response coupled with a poromechanics-based permeability model, and a physics-informed neural network to fuse experimental data with constitutive laws. Economic evaluation uses life-cycle cost analysis and sustainability metrics (embodied energy, carbon footprint). Validity and reliability are addressed through instrument calibration, repeatability trials, and cross-validation of sensor readings against standard tests. Expected findings indicate that the smart concrete exhibits enhanced durability under cyclic wet-dry and hydrostatic loading, improved early-age crack self-healing due to encapsulated microcapsules, and reliable real-time moisture and stress monitoring with low-power wireless transmission within a 200-meter range. The integrated model is anticipated to achieve accurate flood-response predictions (mean absolute error within 8%) and robust risk assessment under varying rainfall-intensity scenarios. Field installations are expected to demonstrate reduced maintenance needs and extended service life for flood-prone structures, with a positive life-cycle cost-benefit ratio when considering resilience gains. The study contributes to knowledge by advancing the design of multifunctional cementitious composites with embedded sensing and adaptive drainage capabilities, enriching the theoretical discourse on smart materials in urban flood resilience, and providing an empirically validated framework for scalable implementation in cities facing similar hydro-meteorological regimes. The principal conclusion is that smart concrete can materially enhance urban flood resilience when integrated with a holistic sensing, data analytics, and design framework that aligns material properties with structural and drainage performance. Recommendations include (i) scaling prototypes to larger structural elements with standardized sensor protocols; (ii) developing guidelines for sustainable production incorporating recycled aggregates and low-emission binders; (iii) establishing regulatory standards for smart-concrete-enabled components in flood-prone urban zones; and (iv) pursuing longitudinal studies to monitor performance under real flood events and evolving climate conditions.