Predicting Concrete Strength Early Age using a Combination of Machine Learning and Electromechanical Impedance with Nano-enhanced Sensors

Huang Ju; Lin Xing; Alaa Hussein Ali; Islam Ezz El-Arab; Ali E A Elshekh; Mohamed Abas Riadh; Nermeen Abdullah; Samia Elattar; Ahmed Hashmi; Elimam Ali; Hamid Asilzad

doi:10.1016/j.envres.2024.119248

Predicting Concrete Strength Early Age using a Combination of Machine Learning and Electromechanical Impedance with Nano-enhanced Sensors

Environ Res. 2024 May 30:119248. doi: 10.1016/j.envres.2024.119248. Online ahead of print.

Authors

Huang Ju¹, Lin Xing², Alaa Hussein Ali³, Islam Ezz El-Arab⁴, Ali E A Elshekh⁵, Mohamed Abas Riadh⁶, Nermeen Abdullah⁶, Samia Elattar⁷, Ahmed Hashmi⁸, Elimam Ali⁹, Hamid Asilzad¹⁰

Affiliations

¹ School of Mechanical Engineering, Chongqing Technology and Business University ,Chongqing, 400067.
² Chongqing Jianzhu College Academy of Construction Management, Chongqing, 400072, China.
³ Building and Construction Techniques Engineering Department, Al-Mustaqbal University College, 51001 Hillah, Babylon, Iraq.
⁴ Structural Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt.
⁵ Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
⁶ Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia.
⁷ Department of Industrial & Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia.
⁸ Department of Architectural Engineering, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi Arabia.
⁹ Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
¹⁰ School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam.

PMID: 38823615
DOI: 10.1016/j.envres.2024.119248

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.

Keywords: Artificial neural network (ANN); Electromechanical impedance; Long Short-Term Memory (LSTM); Nomenclature; Non-Destructive Testing (NDT) Techniques; Piezoelectric sensor; Smart nano.