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

S. R. Mugunthan

Journal of Soft Computing Paradigm, Год журнала: 2023, Номер 5(4), С. 417 - 432

Опубликована: Дек. 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.

Язык: Английский

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

A. Dinesh,

B. Rahul Prasad

Automation in Construction, Год журнала: 2024, Номер 162, С. 105412 - 105412

Опубликована: Апрель 3, 2024

Язык: Английский

Процитировано

20

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

и другие.

Construction and Building Materials, Год журнала: 2024, Номер 440, С. 137370 - 137370

Опубликована: Июль 16, 2024

Язык: Английский

Процитировано

12

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

и другие.

Infrastructures, Год журнала: 2024, Номер 9(1), С. 10 - 10

Опубликована: Янв. 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.

Язык: Английский

Процитировано

9

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

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер unknown, С. 110778 - 110778

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

7

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

и другие.

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112572 - 112572

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

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

Construction and Building Materials, Год журнала: 2025, Номер 479, С. 141475 - 141475

Опубликована: Апрель 26, 2025

Язык: Английский

Процитировано

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

и другие.

International Journal for Computational Civil and Structural Engineering, Год журнала: 2025, Номер 21(1), С. 146 - 156

Опубликована: Март 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

Язык: Английский

Процитировано

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

и другие.

Frontiers in Materials, Год журнала: 2024, Номер 11

Опубликована: Окт. 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.

Язык: Английский

Процитировано

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

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер unknown, С. 111170 - 111170

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

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

и другие.

Sustainability, Год журнала: 2024, Номер 16(24), С. 11139 - 11139

Опубликована: Дек. 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.

Язык: Английский

Процитировано

2