Prediction of confined compressive strength of concrete column strengthened with FRCM composites DOI
Prashant Kumar, Harish Chandra Arora, R. Siva Chidambaram

et al.

Structural Concrete, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 9, 2024

Abstract Nowadays, retrofitting and rehabilitation of deteriorated reinforced concrete structures are becoming a growing need the construction industry instead demolishing aged structures. The application fabric‐reinforced cementitious matrix (FRCM) on existing is one sustainable solutions to retrofit This study used machine learning (ML) models such as linear regression (LR), support vector machines (SVM), adaptive neuro‐fuzzy inference systems (ANFIS) estimate compressive strength (CS) columns wrapped with FRCM. experimental dataset 301 column specimens was collected including input parameters cross‐sectional properties, mechanical properties steel, characteristics FRCM material. Apart from ML models, seven analytical were also compare accuracy precision models. results illustrate that ANFIS model outperformed other established itself dependable precise model. R ‐value 0.9816, whereas ‐values 0.9269 0.9572 achieved by LR SVM respectively. In addition, MAPE value acquired 1.52% which lower than those 73.24%, 60.60%, As higher compared so, developed ANFIS‐based mathematical can be easily predict CS FRCM‐strengthened columns. accurate, economical, fast; utilized applicators structural designers.

Language: Английский

Artificial intelligence in civil engineering DOI
Nishant Raj Kapoor, Ashok Kumar, Anuj Kumar

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 74

Published: Jan. 1, 2024

Citations

11
Tree-based machine learning models for predicting the bond strength in reinforced recycled aggregate concrete DOI

Alireza Mahmoudian,

Maryam Bypour,

Denise‐Penelope N. Kontoni

et al.

Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

Language: Английский

Citations

5
Optimization and Prediction of Colored Pervious Concrete Properties: Enhancing Performance through Augmented Grey Wolf Optimizer and Artificial Neural Networks DOI

Ahmet Tugrul Koc,

Sadık Alper Yıldızel

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112069 - 112069

Published: Feb. 1, 2025

Language: Английский

Citations

0
Application of CNN and MLP models for structural health monitoring: A case study on Saigon Bridge DOI Creative Commons

Thanh Q. Nguyen,

Tu B. Vu,

Niusha Shafiabady

et al.

Journal of low frequency noise, vibration and active control, Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

This paper presents an innovative approach to improve the assessment of mechanical responses in short-span bridges, introducing a novel method with significant implications for bridge engineering. The integrates convolutional neural network (CNN) and multilayer perceptron (MLP) model monitor stiffness degradation spans over time, representing step forward SHM techniques. By harnessing power networks, our enables simultaneous monitoring at multiple measurement points across or various time intervals, providing valuable insights into behavior. Through empirical validation, manuscript demonstrates high accuracy achieved by combined CNN MLP model, augmented spectral density moments, evaluating quality projects throughout their operational lifespan. Moreover, proves highly effective identifying potential hazardous areas on bridges detecting structural damage problematic spans, addressing critical safety concerns infrastructure management. Furthermore, we propose integration data from both non-contact contact sensors further enhance conditions, contributing development more strategies. Additionally, extending scope research encompass different types environmental such as marine environments high-temperature settings, promises elucidate method’s versatility widespread applicability practical scenarios. Future directions include conducting additional real-world tests structures validate feasibility under diverse conditions. In summary, this not only cutting-edge methodology assessing health but also sets stage future advancements technology, profound longevity worldwide.

Language: Английский

Citations

0
Experimental and ANN Analysis of Cold-Formed Steel Build-Up Columns with and without Intermediate Web Stiffeners under Axial Compression DOI

M. Vishnupriyan,

Denise‐Penelope N. Kontoni, Kennedy C. Onyelowe

et al.

Journal of structural design and construction practice., Journal Year: 2025, Volume and Issue: 30(3)

Published: April 8, 2025

Language: Английский

Citations

0
Developing a Fuzzy Expert System for Diagnosing Chemical Deterioration in Reinforced Concrete Structures DOI Creative Commons
Atiye Farahani, Hosein Naderpour,

Gerasimos Konstantakatos

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(18), P. 10372 - 10372

Published: Sept. 16, 2023

The widespread application of reinforced concrete structures in different environmental conditions has underscored the need for effective maintenance and repair strategies. These offer numerous advantages, but are not impervious to deleterious effects chemical deterioration. outcomes this research hold significant implications management system structures. This study proposes utilization a fuzzy expert as means enhancing diagnosis deterioration that is valuable tool engineers decision-makers involved these serves an intelligent can incorporate various symptoms inspection data improve accuracy reliability diagnostic process. By integrating inputs, evaluates 21 points, each representing specific aspect deterioration, on scale ranging from 0 100. numerical representation allows quantification level with denoting minimal 100 indicating severe effectiveness lies its ability process vast amount apply operations 352 rules. rules define relationships between data, type extent. Through computational process, provide insights into 10 distinct types facilitating more precise comprehensive diagnosis. implementation potential revolutionize field diagnosing addressing limitations traditional methods, advanced approach significantly clarity obtain information regarding extent vital developing Ultimately, holds great promise overall durability performance environments.

Language: Английский

Citations

4
Development of Efficient Prediction Model of FRP-to-Concrete Bond Strength Using Curve Fitting and ANFIS Methods DOI
Aman Kumar, Harish Chandra Arora, Krishna Kumar

et al.

Arabian Journal for Science and Engineering, Journal Year: 2023, Volume and Issue: 49(4), P. 5129 - 5158

Published: Oct. 18, 2023

Language: Английский

Citations

4
A Machine Learning Based Model to Assess Flexural Strength of Corroded Reinforced Concrete Beams DOI
Arjun Sharma, Somain Sharma, Kuldeep Kumar

et al.

Lecture notes in civil engineering, Journal Year: 2023, Volume and Issue: unknown, P. 965 - 978

Published: Nov. 2, 2023

Language: Английский

Citations

2
Exploring the Effect of Near-Field Ground Motions on the Fragility Curves of Multi-Span Simply Supported Concrete Girder Bridges DOI Creative Commons

Hassan Soltanmohammadi,

‪Mohammadreza Mashayekhi, Mohammad Mahdi Memarpour

et al.

Infrastructures, Journal Year: 2024, Volume and Issue: 9(2), P. 19 - 19

Published: Jan. 26, 2024

Investigating the impact of near-field ground motions on fragility curves multi-span simply supported concrete girder bridges is main goal this paper. Fragility are valuable tools for evaluating seismic risks and vulnerabilities bridges. Numerous studies have investigated Ground commonly categorized into two sets, based distance recorded station from source: far-field near-field. Studies examining influence records bridge vary depending specific type curve being analyzed. Due to widespread use in Central Southeastern United States, study makes type. This research investigates component column curvatures, bearing deformations, abutment displacements by employing 3-D analytical models conducting nonlinear time history analysis. These illustrate different components. The sets records, 91 78 motions, were obtained compared. findings demonstrate that a greater damaging effect columns abutments than earthquakes. When it comes earthquake more severe at lower intensities, whereas motion stronger higher intensities.

Language: Английский

Citations

0
Estimation of confined compressive strength of LRS‐FRP concrete specimens with computational intelligence DOI Creative Commons

Sleek Chang,

Harish Chandra Arora, Aman Kumar

et al.

ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, Journal Year: 2024, Volume and Issue: 104(10)

Published: Aug. 23, 2024

Abstract Reinforced concrete structures deteriorate due to changes in temperature, corrosion, and attacks of sulfate chloride contents. Retrofitting techniques like fiber‐reinforced polymer (FRP) jacketing, known for their strength corrosion resistance, are increasingly used strengthen retrofit deteriorated structural elements. Large rupture strain (LRS)‐FRP composite, composed polyethylene terephthalate naphthalate, both which have high tensile at been the studies many researchers. This research aims develop a reliable accurate machine learning (ML) model estimate compressive LRS‐FRP confined specimens. A total 303 specimens were gathered after thorough literature review ML models, utilizing linear regression, support vector regression tree, artificial neural network (ANN) algorithms. Additionally, 44 analytical models (AMs) compare performance developed models. The results revealed that ANN was higher among all AMs. R ‐value mean absolute percentage error (MAPE) value 0.9822 6.17%, respectively. sensitivity analysis show height had highest impact followed by diameter specimen, number FRP layers thickness, then LRS‐FRP. ANN‐based mathematical expression is simple easy use predict strengthened

Language: Английский

Citations

0