Scholarlink

<p>The pivotal significance of lateral resisting elements comes to the fore in upholding the security and steadfastness of structures against lateral forces. However, predicting the lateral resistance of these elements is a complex and challenging task that requires considering various factors. Artificial intelligence (AI) techniques have emerged as a promising approach to predicting the lateral resistance of building elements. These techniques can analyze large amounts of data and extract patterns and relationships that are difficult to identify using traditional methods. Consequently, the present research augments the scholarly literature by conducting a methodical examination encompassing all principal facets concerning the lateral stabilizing components of edifices, employing principles derived from artificial intelligence paradigms during the most recent series of publication years. This research also presents an innovative lateral-resistant building taxonomy based on insightful ideas and explores work in various fields that contradict it. To achieve this, we reviewed the ScienceDirect, ASCE, Scopus, IEEE Xplore, and Web of Science databases to conduct this study. Between 2018 and 2024, 4039 papers were aggregated. The established inclusion criteria filtered the articles, resulting in 360 included articles. Six groups were categorized based on (1) moment-resisting frames, (2) braced frames, (3) shear walls, (4) hybrid systems, (5) control systems, and (6) failure mode and damage detection. This review, which adopts a systematic perspective, discusses the apparent benefits of the application of artificial intelligence and offers new research pathways for advancing multidisciplinary synergy. Well-structured, this study will be a handy and noteworthy read for structure researchers and engineers. Furthermore, it draws out issues within the peripheries of ongoing research, stating the value of AI and spotlighting a few recommendations for the adoption of such an approach in handling emerging research gaps significant for a future-proof multidisciplinary collaboration.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Systematic Review on Utilizing Artificial Intelligence in Lateral Resisting Systems of Buildings

  • 期刊论文

  • 发表日期：

    2025年4月27日

查看全文
  • Yasir W. Abduljaleel,
  • Fathoni Usman,
  • Agusril Syamsir,
  • Baraa M. Albaker,
  • Muhammad Imran Najeeb,
  • Mustafa M. Khattab,
  • Safaa N. Saud Al-Humairi

摘要

The pivotal significance of lateral resisting elements comes to the fore in upholding the security and steadfastness of structures against lateral forces. However, predicting the lateral resistance of these elements is a complex and challenging task that requires considering various factors. Artificial intelligence (AI) techniques have emerged as a promising approach to predicting the lateral resistance of building elements. These techniques can analyze large amounts of data and extract patterns and relationships that are difficult to identify using traditional methods. Consequently, the present research augments the scholarly literature by conducting a methodical examination encompassing all principal facets concerning the lateral stabilizing components of edifices, employing principles derived from artificial intelligence paradigms during the most recent series of publication years. This research also presents an innovative lateral-resistant building taxonomy based on insightful ideas and explores work in various fields that contradict it. To achieve this, we reviewed the ScienceDirect, ASCE, Scopus, IEEE Xplore, and Web of Science databases to conduct this study. Between 2018 and 2024, 4039 papers were aggregated. The established inclusion criteria filtered the articles, resulting in 360 included articles. Six groups were categorized based on (1) moment-resisting frames, (2) braced frames, (3) shear walls, (4) hybrid systems, (5) control systems, and (6) failure mode and damage detection. This review, which adopts a systematic perspective, discusses the apparent benefits of the application of artificial intelligence and offers new research pathways for advancing multidisciplinary synergy. Well-structured, this study will be a handy and noteworthy read for structure researchers and engineers. Furthermore, it draws out issues within the peripheries of ongoing research, stating the value of AI and spotlighting a few recommendations for the adoption of such an approach in handling emerging research gaps significant for a future-proof multidisciplinary collaboration.