Soft Computing for Comprehensive Concrete Strength Prediction – A Comparative Study DOI Open Access

S. R. Mugunthan

Journal of Soft Computing Paradigm, Journal Year: 2023, Volume and Issue: 5(4), P. 417 - 432

Published: Dec. 1, 2023

The evolution of concrete strength prediction methodologies has transitioned from empirical formulas based on experimental data to contemporary soft computing approaches. Initially, the mix design was reliant simple relationships between proportions and compressive strength; later, early techniques evolved include statistical models incorporating material properties, curing conditions, environmental variables. advent computational tools artificial intelligence marked a paradigm shift, with accurate crucial for influencing structural integrity, safety, cost-effectiveness in construction. article explores analytical before reviewing application approaches such as fuzzy logic, genetic algorithms, neural networks. integration these hybrid is discussed this research study by highlighting their effectiveness handling complex within parameters. A comparative analysis various methods applied non-structural elements carried out demonstrate diverse applications advantages optimizing designs, enhancing performance, contributing cost time efficiency construction processes.

Language: Английский

Predictive models in machine learning for strength and life cycle assessment of concrete structures DOI

A. Dinesh,

B. Rahul Prasad

Automation in Construction, Journal Year: 2024, Volume and Issue: 162, P. 105412 - 105412

Published: April 3, 2024

Language: Английский

Citations

21
A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete DOI

Tariq Ali,

Mohamed Hechmi El Ouni,

Muhammad Zeeshan Qureshi

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 440, P. 137370 - 137370

Published: July 16, 2024

Language: Английский

Citations

13
Modeling Variability in Seismic Analysis of Concrete Gravity Dams: A Parametric Analysis of Koyna and Pine Flat Dams DOI Creative Commons

Bikram Kesharee Patra,

Rocio L. Segura, Ashutosh Bagchi

et al.

Infrastructures, Journal Year: 2024, Volume and Issue: 9(1), P. 10 - 10

Published: Jan. 5, 2024

This study addresses the vital issue of variability associated with modeling decisions in dam seismic analysis. Traditionally, structural and simulations employ a progressive approach, where more complex models are gradually incorporated. For example, if previous levels indicate insufficient safety margins, advanced analysis is then undertaken. Recognizing constraints evaluating influence various methods essential for improving comprehension effectiveness assessments. To this end, an extensive parametric carried out to evaluate response Koyna Pine Flat dams using solution approaches model complexities. Numerical conducted 2D framework across three software programs, encompassing different system configurations. Additional complexity introduced by simulating reservoir dynamics Westergaard-added mass or acoustic elements. Linear nonlinear analyses performed, incorporating pertinent material properties, employing concrete damage plasticity latter. Modal parameters crest displacement time histories used highlight among selected procedures Finally, recommendations made regarding adequacy robustness each method, specifying scenarios which they most effectively applied.

Language: Английский

Citations

10
Utilizing Construction and Demolition Waste in Concrete as a Sustainable Cement Substitute: A Comprehensive Study on Behavior Under Short-term Dynamic and Static Loads via Laboratory and Numerical Analysis DOI
Mohammad Mohtasham Moein, Komeil Rahmati,

Ali Mohtasham Moein

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 110778 - 110778

Published: Sept. 1, 2024

Language: Английский

Citations

8
Machine Learning Models for Predicting Compressive Strength of Eco-Friendly Concrete with Copper Slag Aggregates DOI
Yaser Moodi, Naser Safaeian Hamzehkolaei, Iman Afshoon

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112572 - 112572

Published: April 1, 2025

Language: Английский

Citations

0
The relationships between ultrasonic P and S wave velocities and resistivity in reinforced concrete DOI
Nevbahar Ekin

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 479, P. 141475 - 141475

Published: April 26, 2025

Language: Английский

Citations

0
A CRITICAL REVIEW OF DEEP LEARNING APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS IN STRUCTURAL ENGINEERING DOI Creative Commons

Manaf Raid Salman,

Marwan Al-Shaikhli,

Hasan Ali Abbas

et al.

International Journal for Computational Civil and Structural Engineering, Journal Year: 2025, Volume and Issue: 21(1), P. 146 - 156

Published: March 31, 2025

Deep learning (DL), a major part of artificial intelligence (AI) is considered as transformational technology in different areas science, such structural engineering. This critical review uncovers the potential contribution deep solving complex issues facing engineering, optimizing design, predicting and monitoring material behaviour, real-time health. Through developed neural network architectures generative adversarial networks (GANs), recurrent (RNNs), convolutional (CNNs), engineers can identify solutions based on traditional deterministic data extraction. However, like computational requirements, model interpretability scarcity are widely adopted. highlights recent advancements, practical applications, limitations proposing pathways for future research to enhance its efficacy integration real-world scenarios

Language: Английский

Citations

0
Modelling the properties of aerated concrete on the basis of raw materials and ash-and-slag wastes using machine learning paradigm DOI Creative Commons
О. В. Руденко,

Darya Galkina,

Marzhan Anuarbekovna Sadenova

et al.

Frontiers in Materials, Journal Year: 2024, Volume and Issue: 11

Published: Oct. 22, 2024

The thermal power industry, as a major consumer of hard coal, significantly contributes to harmful emissions, affecting both air quality and soil health during the operation transportation ash slag waste. This study presents modeling aerated concrete using local raw materials ash-and-slag waste in seismic areas through machine learning techniques. A comprehensive literature review comparative analysis normative documentation underscore relevance feasibility employing non-autoclaved blocks such regions. Machine methods are particularly effective for disjointed datasets, with neural networks demonstrating superior performance complex relationships predicting strength density. results reveal that networks, especially those Bayesian Regularisation, consistently outperformed decision trees, achieving higher regression values (R = 0.9587 R density 0.91997) lower error metrics (MSE, RMSE, RIE, MAE). indicates their advanced capability capture intricate non-linear patterns. concludes artificial robust tool properties, crucial producing curing wall suitable earthquake-resistant construction. Future research should focus on optimizing balance between by enhancing properties utilizing reliable models.

Language: Английский

Citations

2
AI-infused characteristics prediction and multi-objective design of ultra-high performance concrete (UHPC): From pore structures to macro-performance DOI

Wangyang Xu,

Lingyan Zhang,

Dingqiang Fan

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 111170 - 111170

Published: Oct. 1, 2024

Language: Английский

Citations

2
Geo-Environmental Risk Assessment of Sand Dunes Encroachment Hazards in Arid Lands Using Machine Learning Techniques DOI Open Access

Ahmed K. Abd El Aal,

Hossam M. GabAllah,

Hanaa A. Megahed

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(24), P. 11139 - 11139

Published: Dec. 19, 2024

Machine Learning Techniques (MLTs) and accurate geographic mapping are crucial for managing natural hazards, especially when monitoring the movement of sand dunes. This study presents integration MLTs with information systems (GIS) “R” software to monitor dune in Najran City, Saudi Arabia (KSA). Utilizing Linear Support Vector (SVM), Random Forest (RF), Artificial Neural Networks (ANN) nine dune-related variables, this introduces a new Drifting Sand Index (DSI) effectively identifying accumulations. The DSI incorporates multispectral sensors data demonstrates robust capability dynamics. Field surveys spatial analysis were used identify about 100 locations, which then divided into training (70%) validation (30%) sets at random. These models produced thorough encroachment risk map that areas five hazard zones: very low, medium, high, high risk. results show an average 0.8 m/year towards southeast. Performance evaluation utilizing Area Under Curve-Receiver Operating Characteristic (AUC-ROC) approach revealed AUC values 96.2% SVM, 94.2% RF, 93% ANN, indicating RF (AUC = 96.2%) as most effective MLTs. provides valuable insights sustainable development environmental protection, enabling decision-makers prioritize regions mitigation techniques against encroachment.

Language: Английский

Citations

2