Pub Date : 2020-10-28DOI: 10.1201/9780367823085-39
A. Khudhair, H. Li
{"title":"Knowledge-driven holistic decision making supporting multi-objective innovative design","authors":"A. Khudhair, H. Li","doi":"10.1201/9780367823085-39","DOIUrl":"https://doi.org/10.1201/9780367823085-39","url":null,"abstract":"","PeriodicalId":431890,"journal":{"name":"Industry 4.0 – Shaping The Future of The Digital World","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115783465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-28DOI: 10.1201/9780367823085-19
R. Herold, Y. J. Wang, D. Pham, J. Huang, C. Ji, S. Su
{"title":"Using active adjustment and compliance in robotic disassembly","authors":"R. Herold, Y. J. Wang, D. Pham, J. Huang, C. Ji, S. Su","doi":"10.1201/9780367823085-19","DOIUrl":"https://doi.org/10.1201/9780367823085-19","url":null,"abstract":"","PeriodicalId":431890,"journal":{"name":"Industry 4.0 – Shaping The Future of The Digital World","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122502943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-28DOI: 10.1201/9780367823085-53
C. Ogbonnaya, A. Turan, C. Abeykoon
{"title":"Modularization of integrated photovoltaic-fuel cell system for remote distributed power systems","authors":"C. Ogbonnaya, A. Turan, C. Abeykoon","doi":"10.1201/9780367823085-53","DOIUrl":"https://doi.org/10.1201/9780367823085-53","url":null,"abstract":"","PeriodicalId":431890,"journal":{"name":"Industry 4.0 – Shaping The Future of The Digital World","volume":"106 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114124237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-28DOI: 10.1201/9780367823085-12
Tengxiang Su, Haijiang Li
Various machine learning algorithms such as Artificial neural networks (ANNs), Support vector machine (SVM) and Bayesian neural network have been used to improve the accuracy performance of real estate price forecasting. But little research and practice has focused on estimating the price of housing from the construction perspective. Building information modeling (BIM), as a new technology for project information exchange and information management, has been developed for many different industry-specific applications such as automated code checking, energy performance analysis, collaborative design, lifecycle management in the Architecture, Engineering, Construction and Facility Management domain. By integrating BIM and machine learning technologies, this paper proposes a smart comprehensive model which can be used to forecast the price of a new building at the design stage. Furthermore, the smart price estimation engine could be integrated in the whole lifecycle of the building industry.
{"title":"BIM - based machine learning engine for smart real estate appraisal","authors":"Tengxiang Su, Haijiang Li","doi":"10.1201/9780367823085-12","DOIUrl":"https://doi.org/10.1201/9780367823085-12","url":null,"abstract":"Various machine learning algorithms such as Artificial neural networks (ANNs), Support vector machine (SVM) and Bayesian neural network have been used to improve the accuracy performance of real estate price forecasting. But little research and practice has focused on estimating the price of housing from the construction perspective. Building information modeling (BIM), as a new technology for project information exchange and information management, has been developed for many different industry-specific applications such as automated code checking, energy performance analysis, collaborative design, lifecycle management in the Architecture, Engineering, Construction and Facility Management domain. By integrating BIM and machine learning technologies, this paper proposes a smart comprehensive model which can be used to forecast the price of a new building at the design stage. Furthermore, the smart price estimation engine could be integrated in the whole lifecycle of the building industry.","PeriodicalId":431890,"journal":{"name":"Industry 4.0 – Shaping The Future of The Digital World","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127231593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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