Evaluating the influence of Nano-GO concrete pavement mechanical properties on road performance and traffic safety using ANN-GA and PSO techniques.

Journal: Environmental research
Published Date: Sep 5, 2024

Abstract

The burgeoning demand for durable and eco-friendly road infrastructure necessitates the exploration of innovative materials and methodologies. This study investigates the potential of Graphene Oxide (GO), a nano-material known for its exceptional dispersibility and mechanical reinforcement capabilities, to enhance the sustainability and durability of concrete pavements. Leveraging the synergy between advanced artificial intelligence techniques-Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)-it is aimed to delve into the intricate effects of Nano-GO on concrete's mechanical properties. The empirical analysis, underpinned by a comparative evaluation of ANN-GA and ANN-PSO models, reveals that the ANN-GA model excels with a minimal forecast error of 2.73%, underscoring its efficacy in capturing the nuanced interactions between GO and cementitious materials. An optimal concentration is identified through meticulous experimentation across varied Nano-GO dosages that amplify concrete's compressive, flexural, and tensile strengths without compromising workability. This optimal dosage enhances the initial strength significantly, and positions GO as a cornerstone for next-generation premium-grade pavement concretes. The findings advocate for the further exploration and eventual integration of GO in road construction projects, aiming to bolster ecological sustainability and propel the adoption of a circular economy in infrastructure development.

Authors

  • Xuguang Zhang
    Mengniu Institute of Nutrition Science, Shanghai 200126, China.
  • Li Liao
    Department of Computer and Information Sciences, University of Delaware, 18 Amstel Avenue, Newark, 19716, Delaware, USA. lliao@cis.udel.edu.
  • Khidhair Jasim Mohammed
    Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University, Babylon 51001, Iraq. Electronic address: khidhair_aljuboury@yahoo.com.
  • Riadh Marzouki
    Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, 61413 Abha, Saudi Arabia.
  • Ibrahim Albaijan
    Mechanical Engineering Department, College of Engineering at Al Kharj, Prince Sattam Bin Abdulaziz University, Al Kharj 16273, 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.
  • José Escorcia-Gutierrez
    Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia. Electronic address: jescorci56@cuc.edu.co.

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