{"title":"人工智能在未来建筑项目有效执行过程中的应用","authors":"Roozbeh Shakibaei","doi":"10.9734/jemt/2024/v30i61215","DOIUrl":null,"url":null,"abstract":"The construction industry currently constitutes 13% of the global gross domestic product (GDP), with projections indicating an 85% increase in value to $15.5 trillion by 2030. The widespread adoption of information technology (IT) has significantly enhanced the integration of disparate data in construction project environments. Consequently, the construction sector including full construction value chain, is undergoing a transformative phase. The increasing investment in artificial intelligence (AI) makes it impossible to keep pace with its rapid advancements. Hence, this study aims to examine the role of AI in facilitating the effective execution of construction projects in the future. This research employs a document analysis approach and scrutinizes 20 relevant papers from both domestic and international scientific databases. Methodologically, this study adopts an applied research approach, and based on the method of data collection, it is considered a descriptive survey method. Therefore, a questionnaire was designed and distributed among 100 experts and practitioners familiar with AI concepts in Tehran for data collection to conduct a census-style field study. Subsequently, Smart PLS software was employed for data analysis. The findings not only validate the model's reliability, validity and fit but also present solutions and pertinent issues related to challenges concerning AI future role in enhancing project execution efficacy.","PeriodicalId":502721,"journal":{"name":"Journal of Economics, Management and Trade","volume":"26 10","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence in the Effective Execution Process of Construction Projects in the Future\",\"authors\":\"Roozbeh Shakibaei\",\"doi\":\"10.9734/jemt/2024/v30i61215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The construction industry currently constitutes 13% of the global gross domestic product (GDP), with projections indicating an 85% increase in value to $15.5 trillion by 2030. The widespread adoption of information technology (IT) has significantly enhanced the integration of disparate data in construction project environments. Consequently, the construction sector including full construction value chain, is undergoing a transformative phase. The increasing investment in artificial intelligence (AI) makes it impossible to keep pace with its rapid advancements. Hence, this study aims to examine the role of AI in facilitating the effective execution of construction projects in the future. This research employs a document analysis approach and scrutinizes 20 relevant papers from both domestic and international scientific databases. Methodologically, this study adopts an applied research approach, and based on the method of data collection, it is considered a descriptive survey method. Therefore, a questionnaire was designed and distributed among 100 experts and practitioners familiar with AI concepts in Tehran for data collection to conduct a census-style field study. Subsequently, Smart PLS software was employed for data analysis. The findings not only validate the model's reliability, validity and fit but also present solutions and pertinent issues related to challenges concerning AI future role in enhancing project execution efficacy.\",\"PeriodicalId\":502721,\"journal\":{\"name\":\"Journal of Economics, Management and Trade\",\"volume\":\"26 10\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Economics, Management and Trade\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.9734/jemt/2024/v30i61215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economics, Management and Trade","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9734/jemt/2024/v30i61215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial Intelligence in the Effective Execution Process of Construction Projects in the Future
The construction industry currently constitutes 13% of the global gross domestic product (GDP), with projections indicating an 85% increase in value to $15.5 trillion by 2030. The widespread adoption of information technology (IT) has significantly enhanced the integration of disparate data in construction project environments. Consequently, the construction sector including full construction value chain, is undergoing a transformative phase. The increasing investment in artificial intelligence (AI) makes it impossible to keep pace with its rapid advancements. Hence, this study aims to examine the role of AI in facilitating the effective execution of construction projects in the future. This research employs a document analysis approach and scrutinizes 20 relevant papers from both domestic and international scientific databases. Methodologically, this study adopts an applied research approach, and based on the method of data collection, it is considered a descriptive survey method. Therefore, a questionnaire was designed and distributed among 100 experts and practitioners familiar with AI concepts in Tehran for data collection to conduct a census-style field study. Subsequently, Smart PLS software was employed for data analysis. The findings not only validate the model's reliability, validity and fit but also present solutions and pertinent issues related to challenges concerning AI future role in enhancing project execution efficacy.