Cited by Forecasting slipform labor productivity in the construction of reinforced concrete chimneys

Prediction of layered UCS of cemented-gangue backfill material using hybrid intelligent models and resistivity electrical segregation monitoring method DOI

Guorui Feng, Meilin Zhan, Zehua Wang

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112631 - 112631

Published: April 1, 2025

Language: Английский

Citations

Transfer learning for intelligent design of lightweight Strain-Hardening Ultra-High-Performance Concrete (SH-UHPC) DOI

Yixin Zhang,

Qiao Zhang,

Ling-Yu Xu

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 175, P. 106241 - 106241

Published: May 3, 2025

Language: Английский

Citations

Performance optimisation and predictive modelling of rice husk ash recycled concrete under the coupled action of freeze-thaw cycles and chloride erosion: Experimental study and machine learning DOI

Wei Zhang, Zhenhua Duan, Chao Liu

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 481, P. 141467 - 141467

Published: May 4, 2025

Language: Английский

Citations

Application of Intelligent Low‐Cost Accelerometers for Bridge Monitoring With a Deep Learning Approach DOI

Seyyedbehrad Emadi, Seyedmilad Komarizadehasl, Ye Xia

et al.

Structural Control and Health Monitoring, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Despite the crucial role of structural health monitoring (SHM) in ensuring integrity and safety essential infrastructure, its adoption is often limited by high costs traditional sensors. This study introduces an innovative approach for creating intelligent, high‐performing low‐cost accelerometers using a deep learning framework rooted long short–term memory (LSTM) neural networks. Initially, commercial sensors are temporarily installed alongside on bridge to facilitate training process. Once complete, removed, leaving calibrated permanently place perform continuous SHM tasks. In case study, was equipped with array six The efficacy this corroborated through comparative analysis mode shapes eigenfrequencies derived from both sensors, as well intelligent accelerometers.

Language: Английский

Citations

Climate-resilient epoxy asphalt mixture design: An intelligent framework DOI

Ke Zhang, Zhaohui Min, Wei Huang

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103395 - 103395

Published: April 29, 2025

Citations

Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review DOI

B. A. Adewale,

Vincent Onyedikachi Ene,

Babatunde Fatai Ogunbayo

et al.

Published: May 31, 2024

With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application artificial intelligence (AI) holds promise enhancing sustainability in building lifecycle. This systematic literature review addresses current understanding AI's potential to optimize efficiency minimize environmental impact design, construction, operation. A comprehensive synthesis were conducted identify AI technologies applicable sustainable practices, examine their influence, analyze challenges implementation. The was guided by meticulous search strategy utilizing keywords related findings reveal capabilities optimizing through intelligent control systems, enabling predictive maintenance, aiding design simulation. Advanced machine learning algorithms facilitate data-driven analysis prediction, while digital twins provide real-time insights informed decision-making. Furthermore, identifies barriers adoption, including cost concerns, data security risks, presents transformative opportunity enhance built environment, offering innovative solutions optimization environmentally conscious practices. However, addressing technical practical will be crucial successful integration

Language: Английский

Citations

Development Sustainable Concrete with High-volume Wastes Tile Ceramic: Role of Silica Nanoparticles Amalgamation DOI

Zahraa Hussein Joudah, Nur Hafizah A. Khalid, Mohammad Hajmohammadian Baghban

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03733 - e03733

Published: Sept. 4, 2024

Language: Английский

Citations

Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models Augmentation DOI

Yajing Chen,

Urs Liebau,

Shreyas Mysore Guruprasad

et al.

Machine Learning and Knowledge Extraction, Journal Year: 2024, Volume and Issue: 6(4), P. 2494 - 2514

Published: Nov. 4, 2024

Assessing the sustainable development of green hydrogen and assessing its potential environmental impacts using Life Cycle Assessment is crucial. Challenges in LCA, like missing data, are often addressed machine learning, such as artificial neural networks. However, to find an ML solution, researchers need read extensive literature or consult experts. This research demonstrates how customised LLMs, trained with domain-specific papers, can help overcome these challenges. By starting small by consolidating papers focused on LCA proton exchange membrane water electrolysis, which produces hydrogen, applications LCA. These uploaded OpenAI create LlamaIndex, enabling future queries. Using LangChain framework, query model (GPT-3.5-turbo), receiving tailored responses. The results demonstrate that LLMs assist providing suitable solutions address data inaccuracies gaps. ability quickly LLM receive integrated response across relevant sources presents improvement over manually retrieving reading individual papers. shows leveraging fine-tuned empower conduct LCAs more efficiently effectively.

Language: Английский

Citations

Forecasting slipform labor productivity in the construction of reinforced concrete chimneys DOI

Şahin Tolga Güvel

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: unknown, P. 103192 - 103192

Published: Nov. 1, 2024

Language: Английский

Citations