TY - GEN
T1 - A semi-automatic approach to detect structural components from cad drawings for constructing As-Is bim objects
AU - Lu, Qiuchen
AU - Lee, Sanghoon
N1 - Publisher Copyright:
© 2017 American Society of Civil Engineers.
PY - 2017
Y1 - 2017
N2 - With the increasing implementation of building information model (BIM) in operations and maintenance (O&M) management for existing buildings, various new data detection technologies for constructing an as-is BIM have been proposed and developed in past decades. In particular, extracting information from existing CAD documents is still a significant research area in the as-is BIM construction process. Data saved in CAD formats are unstructured and consequently it is hard to extract information and recognize objects in a systematic approach. With the ultimate goal of developing an effective and applicable approach to assist constructing as-is BIM objects, a novel semi-automatic approach is developed to detect structural components from CAD drawings. This approach mainly consists of a data processing part and a data management part. The data processing part defines the location of a structural component through recognizing special symbols in a floor plan and then extracting data from the floor plan using the optical character recognition (OCR) algorithm. The data management part analyzes and takes meaningful information from the extracted data based on predefined outlines. This paper first summarizes state-of-the-art information detection and analysis methods from CAD drawings. Then, the methodology and the prototype application developed in Matlab are introduced and discussed. Moreover, a preliminary set of tests using an office building and the analysis results from the tests are discussed as a case study from the perspectives of applicability and accuracy. Lastly, future works and limitations are also addressed.
AB - With the increasing implementation of building information model (BIM) in operations and maintenance (O&M) management for existing buildings, various new data detection technologies for constructing an as-is BIM have been proposed and developed in past decades. In particular, extracting information from existing CAD documents is still a significant research area in the as-is BIM construction process. Data saved in CAD formats are unstructured and consequently it is hard to extract information and recognize objects in a systematic approach. With the ultimate goal of developing an effective and applicable approach to assist constructing as-is BIM objects, a novel semi-automatic approach is developed to detect structural components from CAD drawings. This approach mainly consists of a data processing part and a data management part. The data processing part defines the location of a structural component through recognizing special symbols in a floor plan and then extracting data from the floor plan using the optical character recognition (OCR) algorithm. The data management part analyzes and takes meaningful information from the extracted data based on predefined outlines. This paper first summarizes state-of-the-art information detection and analysis methods from CAD drawings. Then, the methodology and the prototype application developed in Matlab are introduced and discussed. Moreover, a preliminary set of tests using an office building and the analysis results from the tests are discussed as a case study from the perspectives of applicability and accuracy. Lastly, future works and limitations are also addressed.
UR - http://www.scopus.com/inward/record.url?scp=85021703483&partnerID=8YFLogxK
U2 - 10.1061/9780784480823.011
DO - 10.1061/9780784480823.011
M3 - Conference contribution
AN - SCOPUS:85021703483
SN - 9780784480823
T3 - Congress on Computing in Civil Engineering, Proceedings
SP - 84
EP - 91
BT - Computing in Civil Engineering 2017
A2 - Lin, Ken-Yu
A2 - Lin, Ken-Yu
A2 - El-Gohary, Nora
A2 - El-Gohary, Nora
A2 - Tang, Pingbo
A2 - Tang, Pingbo
PB - American Society of Civil Engineers (ASCE)
T2 - 2017 ASCE International Workshop on Computing in Civil Engineering, IWCCE 2017
Y2 - 25 June 2017 through 27 June 2017
ER -