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<p>Prestressing force plays a key role in ensuring the strength, durability, and serviceability of prestressed concrete (PC) structures. In older PC systems, where embedded sensors are absent, reliable estimation of prestress losses becomes a major challenge. This study proposes a practical, non-destructive method that utilizes static displacement data and a genetic algorithm (GA) to estimate actual prestressing forces in PC beams. The method relies on inverse analysis, where displacement responses from controlled loading conditions are fed into an optimization framework powered by GA. This approach is capable of delivering high-accuracy results, even under conditions with up to 10% measurement error. Experimental validation on PC specimens confirms its robustness, flexibility, and potential for application in real-world settings. By eliminating the need for invasive techniques or costly sensor installations, the proposed strategy provides an efficient solution for evaluating existing PC structures, facilitating maintenance planning, retrofitting efforts, and long-term structural health monitoring.</p>

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Actual prestress force detection of tendons in prestressed beams using genetic algorithm

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Mohamad Sharifi,
  • Mussa Mahmoudi,
  • Mohamad Ghasem Sahab

摘要

Prestressing force plays a key role in ensuring the strength, durability, and serviceability of prestressed concrete (PC) structures. In older PC systems, where embedded sensors are absent, reliable estimation of prestress losses becomes a major challenge. This study proposes a practical, non-destructive method that utilizes static displacement data and a genetic algorithm (GA) to estimate actual prestressing forces in PC beams. The method relies on inverse analysis, where displacement responses from controlled loading conditions are fed into an optimization framework powered by GA. This approach is capable of delivering high-accuracy results, even under conditions with up to 10% measurement error. Experimental validation on PC specimens confirms its robustness, flexibility, and potential for application in real-world settings. By eliminating the need for invasive techniques or costly sensor installations, the proposed strategy provides an efficient solution for evaluating existing PC structures, facilitating maintenance planning, retrofitting efforts, and long-term structural health monitoring.