Cited by Non-linear built environment effects on travel behavior resilience under extreme weather events

Assessment of the vulnerability of urban metro to rainstorms based on improved cloud model and evidential reasoning DOI

Hongyu Chen, Qiping Shen, Zongbao Feng

et al.

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 157, P. 106353 - 106353

Published: Jan. 2, 2025

Language: Английский

Citations

Analysis of influencing factors and carbon emission scenario prediction during building operation stage DOI

Wenhong Luo, Weicheng Liu, Wenlong Liu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134401 - 134401

Published: Jan. 1, 2025

Language: Английский

Citations

Influence mechanism of brick-concrete ratio on the mechanical properties and water permeability of recycled aggregate pervious concrete: Macroscopic and mesoscopic insights DOI

Peichen Cai, Xuesong Mao,

Xiaoyong Lai

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 467, P. 140379 - 140379

Published: Feb. 13, 2025

Language: Английский

Citations

Boosting-Based Machine Learning Applications in Polymer Science: A Review DOI

Ivan Malashin, В С Тынченко, Andrei Gantimurov

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(4), P. 499 - 499

Published: Feb. 14, 2025

The increasing complexity of polymer systems in both experimental and computational studies has led to an expanding interest machine learning (ML) methods aid data analysis, material design, predictive modeling. Among the various ML approaches, boosting methods, including AdaBoost, Gradient Boosting, XGBoost, CatBoost LightGBM, have emerged as powerful tools for tackling high-dimensional complex problems science. This paper provides overview applications science, highlighting their contributions areas such structure-property relationships, synthesis, performance prediction, characterization. By examining recent case on techniques this review aims highlight potential advancing characterization, optimization materials.

Language: Английский

Citations

A review on properties and multi-objective performance predictions of concrete based on machine learning models DOI

Bowen Ni,

Md Zillur Rahman, Shuaicheng Guo

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112017 - 112017

Published: Feb. 1, 2025

Language: Английский

Citations

Application of hybrid machine learning algorithms for life cycle carbon prediction and optimization of buildings: A case study in China DOI

Hongyu Chen, Jingyi Wang,

Qiping Geoffrey Shen

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: 122, P. 106248 - 106248

Published: Feb. 25, 2025

Language: Английский

Citations

Predicting the Alloying Element Yield in Ladle Furnace Using Stacking Ensemble Learning and SHapley Additive exPlanations Analysis DOI

Zicheng Xin, Jiangshan Zhang, Junguo Zhang

et al.

steel research international, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

Accurate prediction of alloying element yield has a significant impact on steel product quality, production costs, and refining efficiency. In this study, the stacking ensemble learning SHapley Additive exPlanations (SHAP) analysis are utilized, along with Bayesian optimization, to develop high‐precision explainable model for yield. Different evaluation criterion is applied compare other existing models. The findings indicate that outperforms models in predicting yield, achieving accuracy 96.1% within an error range ±5%. different variables/base learners results quantitative influence individual each heat clarified using SHAP analysis. This study contributes narrow‐window control molten composition enhances explainability model.

Language: Английский

Citations

Optimizing Urban Block Morphology for Energy Efficiency and Photovoltaic Utilization: Case Study of Wuhan DOI

Ruoyao Wang,

Yanyan Huang, Guoliang Zhang

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(7), P. 1118 - 1118

Published: March 29, 2025

With global carbon emissions continuing to rise and urban energy demands growing steadily, understanding how block morphology impacts building photovoltaic (PV) efficiency consumption has become crucial for sustainable development climate change mitigation. Current research primarily focuses on individual optimization, while block-scale coupling relationships between PV utilization remain underexplored. This study developed an integrated prediction optimization tool using deep learning physical simulation assess design parameters (building morphology, orientation, layout) affect performance. Through a methodology combining modeling, potential assessment, simulation, the quantified parameters, utilization, consumption. Results demonstrate that appropriate forms layouts reduce shadow obstruction, enhance system capability, simultaneously improve reducing The provides improved accuracy, enabling planners scientifically maximize generation minimize use. Extensive experimental validation demonstrates model analytical methods proposed in this will help break through limitations of research, making PV-energy analysis at scale possible, providing scientific basis achieving low-carbon transformation sector.

Language: Английский

Citations

Automated annotation of steel corrosion in UAV-captured images from beneath bridge decks using metaheuristic-optimized computer vision DOI

Jui‐Sheng Chou, Chi‐Yun Liu, Hsin‐Yu Shih

et al.

Structures, Journal Year: 2025, Volume and Issue: 75, P. 108696 - 108696

Published: April 2, 2025

Language: Английский

Citations

Non-linear built environment effects on travel behavior resilience under extreme weather events DOI

Jixiang Liu,

Jianqiang Cui,

Longzhu Xiao

et al.

Transportation Research Part D Transport and Environment, Journal Year: 2025, Volume and Issue: 143, P. 104753 - 104753

Published: April 16, 2025

Language: Английский

Citations