Intelligent Design of Ecological Furniture in Risk Areas based on Artificial Simulation DOI Open Access

Adelfa Torres del Salto Rommy,

Pástor Bryan Alfonso Colorado

Archives of Surgery and Clinical Research, Journal Year: 2024, Volume and Issue: 8(2), P. 062 - 068

Published: Aug. 5, 2024

The study is based on the characterization of different AI models applied in public furniture design analyzing conditions risk, materiality, and integration variables two generative modeling algorithms. As risky since they contain flood-prone areas, low vegetation coverage, underdevelopment infrastructure; therefore, these characterizations are tested through artificial simulation. experimental method laboratory tests various material components their structuring 3D simulators to check resistance risk scenarios. case one most populated areas informal settlement area Northwest Guayaquil, such as Coop, analyzed. Sergio Toral focal point for on-site testing. It concluded that generation a planned scheme ecological with materials responds more effectively territory simulation an advantage can be obtained terms execution time results, thus demonstrating intelligence ideal tool. To generate proposals diverse, innovative, functional environment, but it generates minimum level error specific designs model_01 0.1% 3% high model_02 increasing from 20% 70%. future line research, proposed simulated system all new settlements Guayaquil establish points implementation furniture.

Language: Английский

Graph neural network-assisted evolutionary algorithm for rapid optimization design of shear-wall structures DOI
Yifan Fei, Sizhong Qin, Wenjie Liao

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103129 - 103129

Published: Jan. 15, 2025

Language: Английский

Citations

1

Crack image classification and information extraction in steel bridges using multimodal large language models DOI
Xiaodong Wang,

Qingrui Yue,

Xiaogang Liu

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 171, P. 105995 - 105995

Published: Jan. 28, 2025

Language: Английский

Citations

1

Generative AI in architectural design: Application, data, and evaluation methods DOI
Suhyung Jang, Hyunsung Roh, Ghang Lee

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 174, P. 106174 - 106174

Published: April 4, 2025

Language: Английский

Citations

1

Data-Driven Prediction of Axial Compression Capacity of GFRP-Reinforced Concrete Column Using Soft Computing Methods DOI
Younes Nouri, Ali Reza Ghanizadeh, Farzad Safi Jahanshahi

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111831 - 111831

Published: Jan. 1, 2025

Language: Английский

Citations

0

Intelligent co-design of shear wall and beam layouts using a graph neural network DOI

Jikang Xia,

Wenjie Liao, Bo Han

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 172, P. 106024 - 106024

Published: Feb. 4, 2025

Language: Английский

Citations

0

Generative AI, Large Language Models, and ChatGPT in Construction Education, Training, and Practice DOI Creative Commons
Mostafa Babaeian Jelodar

Buildings, Journal Year: 2025, Volume and Issue: 15(6), P. 933 - 933

Published: March 15, 2025

The rapid advancement of generative AI, large language models (LLMs), and ChatGPT presents transformative opportunities for the construction industry. This study investigates their integration across education, training, professional practice to address skill gaps inefficiencies. While AI’s potential in has been highlighted, limited attention given synchronising academic curricula, workforce development, industry practices. research seeks fill that gap by evaluating AI adoption through a mixed multi-stage methodology, including theoretical conceptualisation, case studies, content analysis application strategic frameworks such as scenario planning, SWOT analysis, PESTEL frameworks. findings show tools enhance foundational learning critical thinking education but often fail develop job-ready skills. Training programmes improve task-specific competencies with immersive simulations predictive analytics neglect leadership Professional benefits from AI-driven resource optimisation collaboration faces barriers like regulatory interoperability challenges. By aligning practical training this highlights create future-ready workforce. provides actionable recommendations integrating domains. These contribute understanding role construction, offering baseline effective responsible adoption.

Language: Английский

Citations

0

Comparative analysis of intelligent retrofit design methods of RC frame structures using buckling-restrained braces DOI
Sizhong Qin, Wenjie Liao,

Zhuang Tan

et al.

Bulletin of Earthquake Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Language: Английский

Citations

0

Review of Optimization Control Methods for HVAC Systems in Demand Response (DR): Transition from Model-driven to Model-free Approaches and Challenges DOI

Ruiying Jin,

Peng Xu, Jiefan Gu

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 113045 - 113045

Published: May 1, 2025

Language: Английский

Citations

0

AI-Powered Safe Egress Time Assessment for Complex Building Fire Evacuation DOI

Tong Lu,

Yanfu Zeng, Zhe Zheng

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 113013 - 113013

Published: May 1, 2025

Language: Английский

Citations

0

AIGC in Design: Critical Thinking Challenges and Opportunities Revealed Through Systematic Review DOI
P. T. Ge,

Danqing Meng,

Fei Fan

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 25 - 43

Published: Jan. 1, 2025

Language: Английский

Citations

0