An as-built data representation system consists two
major parts feasible for design-, construction-, and
finishing- related data manipulation.
1. as-built data retrieval module: The spaces have
to be recorded before, during, and after
construction process. 3D data include the exact
locations and configurations of building parts.
Since the locations are accurate to laser
measurement level, the inter-relationships
between parts are defined correctly. The as-built
data are originally retrieved as point clouds
which count for millions of surface sampling
coordinates. The data are good for as-built
drawing records before and after construction.
The as-built data are also important in the middle
of construction process for quality control.
2. drafting module that incorporate as-built data: A
information management system is required to
integrate the data into, for example, layer
classifications. In one way existing drafting
process can proceed (Fig. 2), and in another a 3D
browsing view is provided to designer for 3D
data manipulation (Fig. 3), as it can be rotated,
scaled, translated, and measured.
The integration and management of 3D point
clouds includes layer management of design data and
file management of web pages. The system has to
incorporate different data formats. Most of the layer
management system stores 2D vector symbols as
graphic components in different layers to create plans,
elevations, sections, or details. The layers are made of
furniture, openings, mechanical/electrical parts,
finishes, structures, partitions, etc. To incorporate asbuilt data, the system is ready to import point clouds in
DXF, and to store and display the data as layers.
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The analysis of construction data
Former research has accomplished 3D comparison
of scan data sets [13]. In this application, scan data are
applied specifically for a better control of renovation
quality. As shown in the system interface (Appendix),
user can access scan data by dates and stages under
design, construction, and post-construction categories.
As a result, the data are applicable along a building’s
life cycle. The data analysis at design stage is related to
drawing production, furniture system evaluation, and
BOM. Registered point clouds verify the real structure
plans through column and partition locations. Interior
dimensions can be measured, noted, and compared
with old drawings in higher accuracy. The plans then
were used to evaluate furniture layout.
Existing kitchen and interior partitions in room
807 and 808 were demolished. The demolition plan
and range images (Fig.4) show the partitions and
ceiling tiles had been removed on Dec. 12. New
partitions started before ceiling jobs. Both ceilings and
HVAC were installed almost at the same time (Fig. 5).
As former researches have been achieved [3,4] in 4D
schedule simulation, this study added point cloud
browser interface for a virtual 3D walk-through as a
schedule inspection aid after scans were registered. Scans were made in full scale to facilitate
measurements in an internet browser mode. When
scans were imported into vector drafting platform, data
were scaled down to 1/100 or 1/200 to create the same
comparison base with drawings. Overlapping design
drawings with point cloud sections was found to be the
most efficient and practical way to detect any possible
old drawing error, comparing to tape or theodolite
measures. Since most building parts were built
orthogonally, the alignment of cloud registration can
also be inspected straight forward.
Comparing design and construction
Post-construction scans helped create finish
drawings to be compared with original design
drawings for differences occurred at several locations.
1. Lobby and corridor: Only ceiling was renovated
at this area. Comparing the design and as-built
data, real wall location was found a 27.83 cm
sideway offset and actual elevator opening width
was 6.38 cm less (Fig. 6).
2. Classrooms: Room 806-808 had demolition,
ceiling, and HVAC jobs. Room dimension was
important for a proper layout of system furniture;
however, scan data showed two errors:
z Column size: The real column size should
be 62cmX80cm, instead of 80cmX80cm. A
20 cm difference existed.
z Wall location: A 39 cm difference existed
between real wall location and drawing data
(Fig. 6). The actual room size was larger
than what drawing indicated. The size error
led to mistakes in BOM and furniture 3. Department office and library: New partitions
were installed to separate a conference room into
office and library. Ceiling remained. Three errors
were found (Fig. 6):
z Wall length: Wrong measurement of wall
length turned out to be the key mistake that
led to other construction problems.
z Wrong partition location: Previous wrong
wall length led to the partition located 31
cm to the left of the designed location.
z Partition ran into opening: Due to the
survey and construction errors, partition ran
into an opening, instead of wall.
All the site jobs were conducted by a graduate
student in 10 working days, out of a period of 48 days.
Although this case is considered as a pilot study,
accurate as-built data and user-friendly internet
interface could have solved design problem initially
and improve construction quality without increasing
working forces.
The study applies 3D range technology to improve
efficiency in interior design and construction with
better accuracy and quality over Internet. The as-built
range data used for visual inspection and production is
feasible to the control of accuracy, coordinates,
working schedule, construction and design comparison,
and follow-up interior maintenance and management.
The study also enables large data sets being browsed
and measured on Internet.
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