Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength DOI Creative Commons
Bashar Alibrahim, Ahed Habib, Maan Habib

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 15, 2025

Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer reliable approach to obtaining this property involve time-consuming procedures. Recent advancements artificial neural networks (ANNs) have shown promise simplifying task by estimating it with high accuracy. Nevertheless, conventional ANNs often require deep achieve acceptable results cases large datasets where generalization required for variety of mixtures. This leads increased training durations susceptibility noise, causing reduced accuracy potential information loss networks. In order address these limitations, study introduces novel multi-lobar network (MLANN) architecture inspired the brain's lobar processing sensory information, aiming improve efficiency concrete strength. The MLANN framework employs various architectures hidden layers, referred as "lobes," each unique arrangement neurons optimize data processing, reduce expedite time. Within context, an developed, its performance evaluated predict concrete. Moreover, compared against two traditional cases, ANN ensemble learning (ELNN). indicated significantly improves estimation performance, reducing root mean square error up 32.9% absolute 25.9% while also enhancing A20 index 17.9%, ensuring more robust generalizable model. advancement model refinement can ultimately enhance design analysis processes civil engineering, leading cost-effective practices.

Language: Английский

Contribution of urban functional zones to the spatial distribution of urban thermal environment DOI
Yang Chen, Jun Yang,

Ruxin Yang

et al.

Building and Environment, Journal Year: 2022, Volume and Issue: 216, P. 109000 - 109000

Published: March 21, 2022

Language: Английский

Citations

155
Research on different types of fiber reinforced concrete in recent years: An overview DOI
Chenggong Zhao, Zhiyuan Wang, Zhenyu Zhu

et al.

Construction and Building Materials, Journal Year: 2022, Volume and Issue: 365, P. 130075 - 130075

Published: Dec. 20, 2022

Language: Английский

Citations

152
Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete DOI
Yanqi Wu,

Yisong Zhou

Construction and Building Materials, Journal Year: 2022, Volume and Issue: 330, P. 127298 - 127298

Published: March 28, 2022

Language: Английский

Citations

137
Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review DOI
Farzin Kazemi, Torkan Shafighfard, Doo‐Yeol Yoo

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(4), P. 2049 - 2078

Published: Jan. 4, 2024

Language: Английский

Citations

55
Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC) DOI
Md Nasir Uddin, Junhong Ye, Bo-Yu Deng

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 72, P. 106648 - 106648

Published: April 25, 2023

Language: Английский

Citations

48
Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach DOI Open Access
Celal Çakıroğlu, Yaren Aydın, Gebrai̇l Bekdaş

et al.

Materials, Journal Year: 2023, Volume and Issue: 16(13), P. 4578 - 4578

Published: June 25, 2023

Basalt fibers are a type of reinforcing fiber that can be added to concrete improve its strength, durability, resistance cracking, and overall performance. The addition basalt with high tensile strength has particularly favorable impact on the splitting concrete. current study presents data set experimental results tests curated from literature. Some best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Machine (LightGBM), Random Forest, Categorical (CatBoost) have been applied prediction reinforced fibers. State-of-the-art performance metrics root mean squared error, absolute error coefficient determination used for measuring accuracy prediction. each input feature model visualized using Shapley Additive Explanations (SHAP) algorithm individual conditional expectation (ICE) plots. A greater than 0.9 could achieved by XGBoost in strength.

Language: Английский

Citations

44
Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants DOI Creative Commons

Muhammad Faisal Javed,

Bilal Siddiq,

Kennedy C. Onyelowe

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102637 - 102637

Published: July 29, 2024

Airborne contaminants pose significant environmental and health challenges. Titanium dioxide (TiO2) has emerged as a leading photocatalyst in the degradation of air compared to other photocatalysts due its inherent inertness, cost-effectiveness, photostability. To assess effectiveness, laboratory examinations are frequently employed measure photocatalytic rate TiO2. However, this approach involves time-consuming requirements, labor-intensive tasks, high costs. In literature, ensemble or standalone models commonly used for assessing performance TiO2 water contaminants. Nonetheless, application metaheuristic hybrid potential be more effective predictive accuracy efficiency. Accordingly, research utilized machine learning (ML) algorithms estimate photo-degradation constants organic pollutants using nanoparticles exposure ultraviolet light. Six metaheuristics optimization algorithms, namely, nuclear reaction (NRO), differential evolution algorithm (DEA), human felicity (HFA), lightning search (LSA), Harris hawks (HHA), tunicate swarm (TSA) were combined with random forest (RF) technique establish models. A database 200 data points was acquired from experimental studies model training testing. Furthermore, multiple statistical indicators 10-fold cross-validation examine established model's robustness. The TSA-RF demonstrated superior prediction among six suggested models, achieving an impressive correlation (R) 0.90 lower root mean square error (RMSE) 0.25. contrast, HFA-RF, HHA-RF, NRO-RF exhibited slightly R-value 0.88, RMSE scores 0.32. DEA-RF LSA-RF while effective, showed marginally 0.85, values 0.45 0.44, respectively. Moreover, SHapley Additive exPlanation (SHAP) results indicated that rates through photocatalysis most notably influenced by factors such reactor sizes, dosage, humidity, intensity.

Language: Английский

Citations

20
An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete DOI Creative Commons
D.P.P. Meddage, Isuri Fonseka, Damith Mohotti

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 449, P. 138346 - 138346

Published: Sept. 17, 2024

Language: Английский

Citations

20
Long-term tracking of urban structure and analysis of its impact on urban heat stress: a case study of Xi’an, China DOI

Kaipeng Huo,

Rui Qin, Jingyuan Zhao

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 174, P. 113418 - 113418

Published: April 9, 2025

Language: Английский

Citations

2
Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms DOI Creative Commons

Hong‐Gen Chen,

Xin Li, Yanqi Wu

et al.

Buildings, Journal Year: 2022, Volume and Issue: 12(3), P. 302 - 302

Published: March 4, 2022

Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse superplasticizer. The prediction results were compared those conventional support vector regression (SVR) four metrics, root mean square error (RMSE), absolute (MAE), percentage (MAPE), correlation coefficient (R2). showed that accuracy reliability LSTM higher R2 = 0.997, RMSE 0.508, MAE 0.08, MAPE 0.653 evaluation metrics 0.973, 1.595, 0.312, 2.469 SVR model. recommended for pre-estimation under given mix ratio before laboratory compression test. Additionally, Shapley additive explanations (SHAP)-based approach performed analyze relative importance contribution variables output strength.

Language: Английский

Citations

62