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<p>Reliable in-situ strength information is critical for construction-stage decisions, yet conventional UPV–UCS correlations are typically established on companion cylinders, which may overestimate the state of cast reinforced members. This study addresses this gap by developing an IoT-enabled embedded ultrasonic sensing system that tracks age-dependent UPV along a fixed internal path (200&#xa0;mm) in cast reinforced concrete from casting to 28&#xa0;days, and by quantifying the persistent discrepancy between embedded-path and cylinder-based surface UPV under identical mix designs. Across all mixes and ages, embedded-path UPV remained lower than surface UPV (ΔUPV = 118–415&#xa0;m/s), suggesting that cylinder-based UPV may provide unconservative strength inference for cast members under the investigated configuration. By pooling embedded UPV with companion-cylinder UCS results, we establish a conservative empirical UCS–UPV model (UCS = 0.3846e0.0011<i>V</i><sub><i>p</i></sub>, <i>R</i><sup>2</sup> = 0.82) with uncertainty metrics (RMSE and MAE). A cable-free LoRa–Wi-Fi architecture with gateway integrity checks and REST-based cloud upload demonstrates the feasibility of periodic near-real-time visualization for curing-stage monitoring, although full communication-performance benchmarking was beyond the scope of this study. Although direct member-strength verification of the cast specimens was not performed, the results support embedded-path UPV as a practical complementary indicator for construction-stage decision making.</p>

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Embedded and surface ultrasonic pulse velocity for in situ strength assessment of cast reinforced concrete during curing using IoT sensing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Gi-Jun Lee,
  • Seong-Won Lee,
  • Suk-Min Kong,
  • Yoseph Byun

摘要

Reliable in-situ strength information is critical for construction-stage decisions, yet conventional UPV–UCS correlations are typically established on companion cylinders, which may overestimate the state of cast reinforced members. This study addresses this gap by developing an IoT-enabled embedded ultrasonic sensing system that tracks age-dependent UPV along a fixed internal path (200 mm) in cast reinforced concrete from casting to 28 days, and by quantifying the persistent discrepancy between embedded-path and cylinder-based surface UPV under identical mix designs. Across all mixes and ages, embedded-path UPV remained lower than surface UPV (ΔUPV = 118–415 m/s), suggesting that cylinder-based UPV may provide unconservative strength inference for cast members under the investigated configuration. By pooling embedded UPV with companion-cylinder UCS results, we establish a conservative empirical UCS–UPV model (UCS = 0.3846e0.0011Vp, R2 = 0.82) with uncertainty metrics (RMSE and MAE). A cable-free LoRa–Wi-Fi architecture with gateway integrity checks and REST-based cloud upload demonstrates the feasibility of periodic near-real-time visualization for curing-stage monitoring, although full communication-performance benchmarking was beyond the scope of this study. Although direct member-strength verification of the cast specimens was not performed, the results support embedded-path UPV as a practical complementary indicator for construction-stage decision making.