Abstract：
To ensure the structural integrity of concrete and prevent unanticipated fracturing, real-time monitoring of early-age concrete\'s strength development is essential, mainly through advanced techniques such as nano-enhanced sensors. The piezoelectric-based electro-mechanical impedance (EMI) method with nano-enhanced sensors is emerging as a practical solution for such monitoring requirements. This study presents a strength estimation method based on Non-Destructive Testing (NDT) Techniques and Long Short-Term Memory (LSTM) and artificial neural networks (ANNs) as hybrid (NDT-LSTMs-ANN), including several types of concrete strength-related agents. Input data includes water-to-cement rate, temperature, curing time, and maturity based on interior temperature, allowing experimentally monitoring the development of concrete strength from the early steps of hydration and casting to the last stages of hardening 28 days after the casting. The study investigated the impact of various factors on concrete strength development, utilizing a cutting-edge approach that combines traditional models with nano-enhanced piezoelectric sensors and NDT-LSTMs-ANN enhanced with nanotechnology. The results demonstrate that the hybrid provides highly accurate concrete strength estimation for construction safety and efficiency. Adopting the piezoelectric-based EMI technique with these advanced sensors offers a viable and effective monitoring solution, presenting a significant leap forward for the construction industry\'s structural health monitoring practices.
摘要：
为了确保混凝土的结构完整性并防止意外破裂，实时监测早龄期混凝土的强度发展至关重要,主要通过纳米增强传感器等先进技术。具有纳米增强传感器的基于压电的机电阻抗（EMI）方法正在成为满足此类监测要求的实用解决方案。本研究提出了一种基于无损检测（NDT）技术和长短期记忆（LSTM）和人工神经网络（ANN）作为混合（NDT-LSTMs-ANN）的强度估计方法，包括几种类型的混凝土强度相关剂。输入数据包括水水泥比，温度,固化时间,和基于内部温度的成熟度，允许通过实验监测从水化和铸造的早期步骤到铸造后28天硬化的最后阶段的混凝土强度的发展。研究了各种因素对混凝土强度发展的影响,利用尖端的方法，将传统模型与纳米增强的压电传感器和NDT-LSTMs-ANN结合使用纳米技术增强。结果表明，混合提供了高度准确的混凝土强度估计的施工安全和效率。采用基于压电的EMI技术与这些先进的传感器提供了一个可行和有效的监测解决方案，为建筑业的结构健康监测实践带来了重大飞跃。
