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230811 ||| eng |
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|a 9783036582184
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|a books978-3-0365-8219-1
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|a 9783036582191
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|a Zhuang, Zheng-Yun
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| 245 |
0 |
0 |
|a Applications of (Big) Data Analysis in A/E/C
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (234 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a building information model
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| 653 |
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|a fan-blade damage
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| 653 |
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|a 3D point cloud
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| 653 |
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|a cosine similarity
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| 653 |
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|a predictive regression modelling
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| 653 |
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|a vibration data
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| 653 |
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|a waste PE
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| 653 |
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|a n/a
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| 653 |
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|a wind turbines
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| 653 |
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|a water-to-binder ratio (W/B ratio)
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| 653 |
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|a high performance concrete (HPC)
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| 653 |
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|a deep learning
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| 653 |
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|a single shot multibox detector (SSD)
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| 653 |
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|a decision-making
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| 653 |
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|a History of engineering & technology / bicssc
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| 653 |
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|a real-time monitoring
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| 653 |
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|a mechanical condition monitoring system
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| 653 |
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|a shapley additive explanation
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| 653 |
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|a reliability analysis
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| 653 |
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|a cement mortar
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| 653 |
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|a foundation pit
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a data quality dimensions
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| 653 |
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|a durability
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| 653 |
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|a energy generation
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| 653 |
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|a heat map
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| 653 |
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|a object detection
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| 653 |
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|a correlation analysis
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| 653 |
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|a vibration analysis
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| 653 |
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|a accelerometer
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| 653 |
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|a experimental parameters
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| 653 |
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|a recycling of waste materials
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| 653 |
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|a semiotic framework
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| 653 |
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|a data-driven analysis
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| 653 |
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|a RC slab-column structure
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| 653 |
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|a fault diagnosis
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| 653 |
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|a pairwise comparison analysis
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| 653 |
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|a maintenance by prediction
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| 653 |
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|a pre-project material testing
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| 653 |
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|a green energy
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| 653 |
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|a variable transform
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| 653 |
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|a artificial intelligence
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| 653 |
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|a environmental temperature
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| 653 |
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|a semantic segmentation
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| 653 |
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|a big-data analytics
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| 653 |
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|a construction image
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| 653 |
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|a steel assembly bracing
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| 653 |
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|a wireless sensor network
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|a Monte Carlo simulation
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| 653 |
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|a historic building
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| 653 |
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|a highway data quality assessment
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| 700 |
1 |
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|a Yang, Ying-Wu
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| 700 |
1 |
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|a Hsu, Ming-Hung
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| 700 |
1 |
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|a Zhuang, Zheng-Yun
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
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|a 10.3390/books978-3-0365-8219-1
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| 856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/7616
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/112499
|z DOAB: description of the publication
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|a 900
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|a 363
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|a This Special Issue (SI), entitled "Application of (Big) Data Analysis in A/E/C (architecture, engineering, and construction)", in Buildings has published quality papers outlining novel contributions of the application of big data theory, modeling techniques, and methodologies as a tool for leveraging data analytics in the building industry. Contributions have covered related topics from various application domains of the global A/E/C industry, including structures/structural engineering, materials/geotechnical engineering, construction design/measure, IS/BIM, transport/energy planning and building management, etc. As can be seen in the editorial essay, some domains have been addressed with more emphasis, but some are not. In addition to the editorial essay, the published research works have also touched almost all phases of (big) data analytics, including data collection and curation, data pre-processing, data analysis, prediction, (data-driven) decision making and decision supports, etc., except that related topics about forecasting are still missing. This allows for identifying 'hot zones' of research, in which follow-up research and extended studies should be performed, and 'cold zones', awaiting the utilization and novel application of data and theories/models.
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