A study on civil engineering construction simulation technology based on artificial intelligence for civil buildings

Abstract This research delves into utilizing Artificial Intelligence (AI) in civil engineering, specifically construction simulations. The primary objective is to reconcile the discrepancies between conventional structural analysis techniques and the actual performance of structures. By integrating AI technologies—neural networks, cellular automata, and support vector machines— with data mining, this research proposes a novel approach for simulating civil building constructions. Traditional methods, often constrained by underlying assumptions, fail to capture the complex behaviors of engineering structures accurately. The developed AI simulation framework utilizes experimental and on-site data to construct digital models, enabling precise prediction of structural behaviors without the limitations of traditional assumptions. Results indicate that AI-enhanced simulations can significantly improve the accuracy of structural analysis, demonstrating potential for wider application in civil engineering. This advancement promises to enhance the predictability, efficiency, and safety of civil constructions, marking a significant step forward in the field.

Authors

Institutions

Publication Details

Journal
Journal of Physics Conference Series
Published
2024-08-01
DOI
https://doi.org/10.1088/1742-6596/2816/1/012071
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A study on civil engineering construction simulation technology based on artificial intelligence for civil buildings

Jiahao Zhang, Liwei Fan, Haoyun Sun, Jiachen Yi et al.
Journal of Physics Conference Series
BIM and Construction Integration
article

A study on civil engineering construction simulation technology based on artificial intelligence for civil buildings

Jiahao Zhang, Liwei Fan, Haoyun Sun, Jiachen Yi, Qian Liu
article en

Abstract

Abstract This research delves into utilizing Artificial Intelligence (AI) in civil engineering, specifically construction simulations. The primary objective is to reconcile the discrepancies between conventional structural analysis techniques and the actual performance of structures. By integrating AI technologies—neural networks, cellular automata, and support vector machines— with data mining, this research proposes a novel approach for simulating civil building constructions. Traditional methods, often constrained by underlying assumptions, fail to capture the complex behaviors of engineering structures accurately. The developed AI simulation framework utilizes experimental and on-site data to construct digital models, enabling precise prediction of structural behaviors without the limitations of traditional assumptions. Results indicate that AI-enhanced simulations can significantly improve the accuracy of structural analysis, demonstrating potential for wider application in civil engineering. This advancement promises to enhance the predictability, efficiency, and safety of civil constructions, marking a significant step forward in the field.

Journal of Physics Conference SeriesVol. 2816(1)
Xi'an University of Architecture and Technology (CN), University of South Australia (AU)
Sustainable cities and communities
Openalex Percentile: Top 87%
BIM and Construction Integration
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A study on civil engineering construction simulation technology based on artificial intelligence for civil buildings — Jiahao Zhang, Liwei Fan, et al. · Journal of Physics Conference Series (2024) | TGRS Research Map | TGRS