Predicting concrete strength early age using a combination of machine learning and electromechanical impedance with nano-enhanced sensors.

Journal: Environmental research
PMID: 38823615

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.

Authors

  • Huang Ju
    School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, 400067, China.
  • Lin Xing
    Chongqing Jianzhu College Academy of Construction Management, Chongqing, 400072, China. Electronic address: xinglinl116@163.com.
  • Alaa Hussein Ali
    Building and Construction Techniques Engineering Department, Al-Mustaqbal University, 51001, Hillah, Babylon, Iraq. Electronic address: alaahussein@uomus.edu.iq.
  • Islam Ezz El-Arab
    Structural Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt.
  • Ali E A Elshekh
    Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
  • Mohamed Abbas
    Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
  • Nermeen Abdullah
    Department of Industrial & Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia.
  • Samia Elattar
    Department of Industrial & Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia.
  • Ahmed Hashmi
    Department of Architectural Engineering, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi Arabia.
  • Elimam Ali
    Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
  • Hamid Assilzadeh
    Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam; School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam; Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, 600077, India; Faculty of Architecture and Urbanism, UTE University, Calle Rumipamba S/N and Bourgeois, Quito, Ecuador. Electronic address: hamidassilzadeh@duytan.edu.vn.

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