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

被引：0

作者：

Zhang, Xuguang ^{[1
,2
]}

Liao, Li ^{[2
]}

Mohammed, Khidhair Jasim ^{[3
]}

Marzouki, Riadh ^{[4
]}

Albaijan, Ibrahim ^{[5
]}

Abdullah, Nermeen ^{[6
]}

Elattar, Samia ^{[6
]}

Escorcia-Gutierrez, Jose ^{[7
]}

机构：

[1] Chongqing Jiaotong Univ, Sch Civil Engn, Chongqing 400074, Peoples R China

[2] Chongqing Jianzhu Coll, Sch Transportat & Municipal Engn, Chongqing 400072, Peoples R China

[3] Al Mustaqbal Univ, Dept Air Conditioning & Refrigerat Tech Engn, Babylon 51001, Iraq

[4] King Khalid Univ, Fac Sci, Dept Chem, POB 9004, Abha 61413, Saudi Arabia

[5] Prince Sattam Bin Abdulaziz Univ, Coll Engn Al Kharj, Mech Engn Dept, Al Kharj 16273, Saudi Arabia

[6] Princess Nourah Bint Abdulrahman Univ, Coll Engn, Dept Ind & Syst Engn, POB 84428, Riyadh 11671, Saudi Arabia

[7] Univ Costa, Dept Computat Sci & Elect, CUC, Barranquilla 080002, Colombia

来源：

ENVIRONMENTAL RESEARCH | 2024年 / 262卷

基金：

中国国家自然科学基金;

关键词：

Nano graphene oxide (GO); Concrete pavements; Artificial neural networks (ANN); Genetic algorithms (GA); Particle swarm optimization (PSO); Sustainable infrastructure; AXIAL COMPRESSIVE BEHAVIOR; HIGH-STRENGTH CONCRETE; GRAPHENE-OXIDE; RHEOLOGICAL PROPERTIES; ASPHALT BINDERS; SHEAR-STRENGTH; COLUMNS; SYSTEM; STRAIN; BEAMS;

D O I：

10.1016/j.envres.2024.119884

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：20