Archives of Civil and Mechanical Engineering, Год журнала: 2024, Номер 24(4)
Опубликована: Авг. 28, 2024
Язык: Английский
Buildings, Год журнала: 2024, Номер 14(1), С. 190 - 190
Опубликована: Янв. 11, 2024
Machine learning (ML) algorithms have been widely used in big data prediction and analysis terms of their excellent regression ability. However, the accuracy different ML varies between problems sets. In order to construct a model with optimal for fly ash concrete (FAC), such as genetic programming (GP), support vector (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) adaptive network-based fuzzy inference system (ANFIS) were selected this study; particle swarm optimization (PSO) algorithm was also optimize structure hyperparameters each algorithm. The statistical results show that performance assembled is better than an NN-based addition, PSO can effectively improve algorithms. comprehensive analyzed using Taylor diagram, PSO-XGBoost has best performance, R2 MSE equal 0.9072 11.4546, respectively.
Язык: Английский
Процитировано
8
Modeling Earth Systems and Environment, Год журнала: 2025, Номер 11(1)
Опубликована: Янв. 14, 2025
Язык: Английский
Процитировано
1
Cleaner Engineering and Technology, Год журнала: 2025, Номер unknown, С. 100899 - 100899
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1
Engineering Reports, Год журнала: 2023, Номер 5(9)
Опубликована: Май 23, 2023
Abstract Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers designers are generally faced with slightest change conditions or objectives project, which makes it challenging to choose optimal among several ones. Besides, experimental examination all them requires time high costs. Hence, an efficient approach utilize artificial intelligence (AI) techniques predict optimize real‐world problems technology. Despite large body publications this field, there few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review lists, summarizes, categorizes most widely used machine learning methods, meta‐heuristic algorithms, hybrid approaches issues. To end, 457 considered during recent decade highlight annual trend/active journals/top researchers/co‐occurrence key title words/countries' participation/research hotspots. In addition, AI classified into distinct clusters using VOSviewer clustering visualization identify application scope their relationship through link strength. The findings can be beacon help researchers future research on advanced
Язык: Английский
Процитировано
21
Sustainability, Год журнала: 2023, Номер 15(12), С. 9715 - 9715
Опубликована: Июнь 18, 2023
The realistic determination of damage estimation and building performance depends on target displacements in performance-based earthquake engineering. In this study, were obtained by performing pushover analysis for a sample reinforced-concrete model, taking into account 60 different peak ground accelerations each the five stories. Three estimation, such as limitation (DL), significant (SD), near collapse (NC), acceleration numbers stories, respectively. It aims to develop an artificial neural network (ANN)-based sustainable model predict under seismic risks mid-rise regular buildings, which make up large part existing stock, using all data obtained. For purpose, hybrid structure was established with particle swarm optimization algorithm (PSO), structure’s hyper parameters optimized. models created order most successfully. found that ANN particles best position revealed produced successful results calculation score. 99% DL SD NC determining buildings. also should be used estimating risks.
Язык: Английский
Процитировано
18
Buildings, Год журнала: 2023, Номер 13(12), С. 3100 - 3100
Опубликована: Дек. 13, 2023
The addition of Ladle Furnace Slag (LFS) to concrete modifies its compressive strength and modulus elasticity consequently impacts their relationship. This research evaluated both properties at 28, 90, 180 days in mixes produced with 5%, 10%, 20% two LFS types, stabilized non-stabilized. relationship between them was then analyzed through these experimental results by adopting a statistical approach. A three-way analysis variance revealed that were affected differently. Thus, the effect each content on features varied depending composition pre-treatment. Furthermore, also influenced age concrete. These facets implied when analyzing mechanical properties, monotonic correlations stronger than linear ones, reaching values 0.90 1.00. Therefore, double reciprocal regression models most precise ones for expressing as function strength. model accuracy further enhanced discriminating based type introducing predictive variable. With all considerations, average deviations estimated 1–3% maximum 4–7% reached, well R2 coefficients up 97%. aspects are central development models.
Язык: Английский
Процитировано
15
Heliyon, Год журнала: 2024, Номер 10(5), С. e27179 - e27179
Опубликована: Фев. 27, 2024
Seismic design principles advocate for simple and regular structures to minimize earthquake damage. However, this frequently does not lead unique aesthetically pleasing designs, leading some engineers select irregular despite the potential risks. The primary aim of investigation is achieve optimal torsional irregularity coefficients planar reinforced concrete (RC) frames under static dynamic loads, utilizing a 3D 6-layer model. Structural ground vibration analysis was conducted using ETABS software. By imposing limits on each layer frame layout, we subsequently applied combination artificial neural networks (ANN) with particle swarm optimization (PSO) algorithm, namely ANN-PSO, address size distribution issue across structure. variables included dimensions columns located in layout. results demonstrate that ANN-PSO algorithm optimizes cross-sectional area significant variations. torsion inequality rule optimized solution closely approach minimum values. orientations slightly differ from pre-optimized scheme. In scheme, Y-direction meet requirements, preventing any irregularities occurring. research presented an effective method, including innovative finite element method (FEM), designing RC structures. findings provided practical fulfill regularity criteria, indicating proposed economical safe earthquake-prone areas. outcomes present study highlighted framework designs while minimizing damage during earthquakes.
Язык: Английский
Процитировано
5
Cleaner Materials, Год журнала: 2024, Номер 13, С. 100258 - 100258
Опубликована: Июнь 27, 2024
Geopolymer concrete emerges as a sustainable and durable alternative to conventional concrete, addressing its high carbon footprint enhanced durability. The distinct properties of geopolymer governed by supplementary cementitious materials alkaline activators, promise reduced environmental impact improved structural resilience. However, complex composition complicates the prediction mechanical such elastic modulus, crucial for applications. This study introduces an innovative approach using eXtreme Gradient Boosting (XGBoost) technique integrated with multi-objective grey wolf optimizer model modulus concrete. By dynamically selecting influential features optimizing accuracy, this methodology advances beyond traditional empirical models, which fail capture nonlinear interactions intrinsic Utilizing comprehensive database gathered from extensive literature, 22 potential variables were examined that influence concrete's modulus. After mitigating multicollinearity hyperparameters via Bayesian optimization, six XGBoost models developed different combinations input variables, revealing compressive strength total water content pivotal predictors. findings illustrate models' precision, trade-off between accuracy simplicity visualized through relationship number error. culminates in user-friendly graphical user interface enables easy fosters educational engagement. interface, available online, underscores practicality accessibility advanced machine learning predictions. Overall, research not only provides robust predictive framework optimized but also enhances understanding underlying determinants, contributing advancement construction materials.
Язык: Английский
Процитировано
5
Lecture notes in civil engineering, Год журнала: 2025, Номер unknown, С. 801 - 811
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Mathematics, Год журнала: 2025, Номер 13(7), С. 1021 - 1021
Опубликована: Март 21, 2025
Tunnel infrastructures worldwide face escalating deterioration challenges due to aging materials, increasing load demands, and exposure harsh environmental conditions. Accurately predicting the onset progression of is paramount for ensuring structural safety, optimizing maintenance interventions, prolonging service life. However, complex interplay environmental, material, operational factors poses significant current predictive models. Additionally, they are constrained by small datasets a narrow range tunnel elements that limit their generalizability. This paper presents novel hybrid metaheuristic-based regression tree (REGT) model designed enhance accuracy robustness predictions. Leveraging metaheuristic algorithms’ strengths, developed method jointly optimizes critical hyperparameters identifies most relevant features prediction. A comprehensive dataset encompassing material properties, stressors, traffic loads, historical condition assessments was compiled development. Comparative analyses against conventional trees, artificial neural networks, support vector machines demonstrated consistently outperformed baseline techniques regarding While trees classic machine learning models, no single variant dominated all elements. Furthermore, optimization framework mitigated overfitting provided interpretable insights into primary driving deterioration. Finally, findings this research highlight potential models as powerful tools infrastructure management, offering actionable predictions enable proactive strategies resource optimization. study contributes advancing field modeling in civil engineering, with implications sustainable management infrastructure.
Язык: Английский
Процитировано
0