Prediction of the shear strength of lightweight concrete beams without web reinforcement based on a machine learning model optimized by a genetic algorithm

Structures, Journal Year: 2024, Volume and Issue: 65, P. 106738 - 106738

Published: June 15, 2024

Language: Английский

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Deep learning-aided optimization framework for hybrid braced structures to support life-cycle cost-based design

Engineering Structures, Journal Year: 2025, Volume and Issue: 327, P. 119603 - 119603

Published: Jan. 9, 2025

Language: Английский

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Prediction of seismic performance of steel frame structures: A machine learning approach

Structures, Journal Year: 2024, Volume and Issue: 69, P. 107547 - 107547

Published: Oct. 15, 2024

Language: Английский

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A machine learning approach used to predict the peak displacement, base shear and fundamental frequency of multi-storey steel structures under seismic excitation

Structures, Journal Year: 2025, Volume and Issue: 73, P. 108367 - 108367

Published: Feb. 12, 2025

Language: Английский

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Energy-based seismic design procedure of steel frames with low-prestressing based resilient braces

Structures, Journal Year: 2025, Volume and Issue: 72, P. 108311 - 108311

Published: Jan. 27, 2025

Language: Английский

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A Deep Learning-based Method for Identifying the Dynamic Stability of Single-layer Spherical Reticulated Shells

KSCE Journal of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 100192 - 100192

Published: Feb. 1, 2025

Language: Английский

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The damage-based inelastic displacement ratio for performance-based design of fully self-centering systems under pulse-like near-fault earthquake ground motions

Structures, Journal Year: 2024, Volume and Issue: 64, P. 106513 - 106513

Published: May 7, 2024

Language: Английский

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Intelligent Life Cycle Cost-Based Framework for Seismic Design of Conventional Structures

Buildings, Journal Year: 2024, Volume and Issue: 14(9), P. 2597 - 2597

Published: Aug. 23, 2024

It is well understood that the dominant approach in seismic design of structures to reduce initial cost while meeting required safety level, as dictated by compliance codes. Nevertheless, this often overlooks long-term costs are incurred over lifetime structures. A comprehensive thus for a based on life cycle (LCC), where both and considered. While LCC-based has been employed regular structures, irregular have not received adequate attention. This research aims highlight impact irregularity LCC optimization tall To do this, bi-objective heuristic framework developed balance costs. The used analyze six steel setback with 7, 10, 13 stories. all designed meet performance level. findings show reveal higher sensitivity variations compared which mainly buildings above We also reducing LCCs requires increase structures; example, 13-story 17% resulted approximately 48% 40% reductions LCCs, respectively. Overall, our results confirm more than those ones; an important finding should be considered

Language: Английский

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Life-Cycle Performance Modeling for Sustainable and Resilient Structures under Structural Degradation: A Systematic Review

Buildings, Journal Year: 2024, Volume and Issue: 14(10), P. 3053 - 3053

Published: Sept. 25, 2024

The performance of structures degrades during their service life due to deterioration and extreme events, compromising the social development economic growth structure infrastructure systems. Buildings bridges play a vital role in socioeconomic built environment. Hence, it is essential understand existing tools methodologies efficiently model these cycle. In this context, paper aims explore literature on life-cycle modeling, assessment, enhancement, decision making buildings bridge systems under events for sustainable resilient main objectives are (1) systematically review modeling based PRISMA methodology, (2) provide bibliometric analysis assessed journal articles, (3) perform an included articles identified components (4) discussion utilized tools, techniques, methodologies, frameworks context. provided systematic subsequent discussions could overview reader regarding individual management events.

Language: Английский

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Machine learning-based design of double corrugated steel plate shear walls

International Journal of Structural Integrity, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 5, 2024

Purpose In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs. Design/methodology/approach ML Various techniques, including linear regression (LR), support vector (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) artificial neural network (ANN), utilized. The models trained using a dataset 462 numerical experimental samples. Numerical generated analyzed finite element (FE) software Abaqus. These underwent push-over analysis, subjecting them pure conditions by applying target displacement solely top walls without interaction from frame. input data encompassed eight survey variables: geometric values material types. characterization FE was randomly within logical range for each variable. training testing phases 90 10% data, respectively. predicted two output targets: DCSWs likelihood buckling. Accurate predictions in these areas contribute efficient lateral enhancement structures. An ensemble method enhance prediction accuracy, incorporating select algorithms. Findings proposed model achieved remarkable 98% R-score estimating strength corresponding accuracy predicting buckling occurrences. Among all tested, XGBoost demonstrated best performance. Originality/value first time,

Language: Английский

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