MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641095660 A) filed by Varadaraj K S; Archana K; Kavyashree S; Dr. Saketh Shetty; and Dr. K. E. Prakash on August 07, 2026, for Smart Ai-Based Device For Real-Time Concrete Quality And Strength Evaluation..
Inventors include Varadaraj K S; Archana K; Kavyashree S; Dr. Saketh Shetty; and Dr. K. E. Prakash.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: ABSTRACT [505] Accurate, timely assessment of in-situ concrete quality and compressive strength, characterized by dependable detection of internal voids, honeycombing, segregation, and curing-related strength deficiencies, constitutes a foundational requirement for construction quality assurance, structural safety certification, and infrastructure maintenance; however, conventional assessment predominantly relies on destructive core-extraction testing or operator-dependent non- destructive methods such as manual rebound hammer and ultrasonic pulse velocity testing, both requiring extended laboratory turnaround, skilled operator interpretation, and providing only localized, non-continuous quality assessment unsuited to real-time, on-site decision-making during active construction. [510] Existing concrete testing devices exhibit critical limitations including reliance on single-modality measurement lacking correlation between multiple non-destructive indicators, absence of automated, learning-based strength-prediction models calibrated to mix-specific and curing-specific variation, inability to deliver real-time, on-site quality classification and defect localization during active pours and curing, and lack of adaptive calibration suited to diverse concrete mix designs and environmental curing conditions, collectively rendering existing tools inadequate for rigorous, real-time construction quality assurance. [515] The integration of multi-sensor non-destructive data acquisition, deep learning based feature fusion, real-time compressive-strength prediction, and automated defect classification presents transformative opportunities for developing a portable, automated concrete quality evaluation capability that provides operator-independent, statistically validated, real-time strength and defect assessment accessible to construction personnel without dependence on laboratory core testing or specialist NDT interpretation. [520] The present invention describes a Smart AI-Based Device for Real-Time Concrete Quality and Strength Evaluation comprising a multi-sensor data acquisition module integrating ultrasonic pulse velocity transducers, rebound hammer impact sensors, surface resistivity electrodes, and embedded temperature-humidity sensors; a signal processing and feature extraction module that conditions and fuses acquired multi-modal sensor signals; a deep learning based compressive-strength prediction module trained on a curated corpus of paired sensor-signal and destructive core-test strength measurements; a real-time quality classification and defect detection module that identifies voids, honeycombing, and segregation; and a reporting and cloud-analytics module that delivers immediate, site-actionable quality and strength assessment to construction personnel. [525] Comprehensive evaluation of the device across a benchmark corpus of instrumented concrete specimens and field pours demonstrated compressive- strength prediction accuracy of 96.2 ± 1.5 percent relative to destructive core-test reference values with a coefficient of determination of 0.97, internal defect classification accuracy of 91.4 ± 2.2 percent, mean real-time inference latency of under 200 milliseconds following sensor data acquisition, and assessment turnaround time reduction of over 85 percent compared to conventional laboratory core-extraction testing. [530] These findings confirm that the Smart AI-Based Device for Real- Time Concrete Quality and Strength Evaluation constitutes a significant advancement in automated, non-destructive construction quality assurance, with deployment applicability spanning building and infrastructure construction sites, precast concrete manufacturing facilities, structural rehabilitation and inspection programmes, and civil engineering quality-control laboratories, thereby addressing the substantial unmet need for scalable, accurate, and real-time concrete quality and strength evaluation independent of destructive testing or specialist operator availability.
Disclaimer: Curated by HT Syndication.
