Whi te Pa per # 1 2 1
Authors: Carlos, Carl, Paul, Dennis
B r eakt h ro u gh St r at eg y C o mmit t ee
Effortless Productivity Tracking
1. Need and Potential
In principle, construction productivity measures examine the efficiency of construction production,
including the process of work crews utilizing tools, equipments, and certain working methods to install materials at
the workface according to engineering information. Therefore, to improve the productivity of any onsite operation, it
is important to rapidly record relevant data on utilized resources and methods in the operations and the output
quantities produced by the operations. Timely and accurate productivity information brings an immediate awareness
of project specific issues to construction management, and empowers construction management to take prompt
corrective actions, thus avoiding costly delays. There is sufficient evidence to suggest that onsite productivity
measurement is the premise for making any productivity improvement decisions.
2. Background and Gaps
Typical onsite productivity measurement methods include project level information systems, direct
observation methods, and survey/interview based methods, though the practices of these methods vary significantly
across different contractors. Each method is engineered towards measuring certain aspects of construction
production and complementing each other. Project level information systems, such as electronic cost reports and unit
rate reports, are used to measure the input and output in construction productions, therefore generating productivity
measures such as worker hour per output quantities. From this perspective, such systems can only reveal issues
pertaining to the global outcome in production. The process of achieving such productivity performance is not
measured in these systems. In order to measure the process of construction production, either observation based
methods for observing the process or survey/interview based methods for asking workers involved in the process of
production need to be used. Direct observation for work measurement includes a set of scientific methods that are
adapted from industrial engineering. These scientific methods are used to develop factual records of how the onsite
operations are done. Survey and interview methods solicit suggestions to explain why some of the problems
observed in the operation process happen. The information gathered through the above three methods are then used
to support productivity improvement decision making, which completes a typical framework of productivity
measurement for improvement in the construction industry (Oglesby et al. 1989).
Although this productivity measurement framework does offer a plausible solution to improve onsite
operations, one of the major limitations is that most of these methods are manually intensive, resulting in relatively
outdated information and expensive data collection systems (McCullouch 1997; Cheok et al. 2000). For example,
because of the manual efforts required in input and output quantity collection, the productivity information in the
project level information systems is often slowly updated (every one to two weeks), leaving such systems only
suitable for the purpose of macro project control, such as cost tracking, and impractical for supporting rapid
response to problems that cause productivity loss in ongoing operations. The same limitations apply to other
methods in this productivity measurement framework. It makes eminent sense, therefore, to attempt to develop
automated productivity measurement methods as a crucial step towards onsite productivity improvement. At the
same time, the recent emergence and rapid development of site sensing technologies, such as real-time localization,
identification technologies, 3D imaging systems, and wireless construction cameras, have provided a steady stream
of improved productivity measurement methods for construction organizations. To this end, the potential impact of
automated site sensing for productivity measurement is enormous both to the industry and nation in terms of support
of overall competitiveness and optimizing construction spending.
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3. State of the art
Automated project progress tracking, in contrast to manually based quantity collection efforts, aims to
automate the measurement of physical quantities in-place by using spatial sensing technologies. This is feasible
because virtually the final product of every construction project is a tangible physical object. An intuitive way to
assess the project progress would be to geometrically compare the as-built condition with the planned condition.
This concept has been supported by a number of research studies. Cheok et al. (2000), for example, demonstrated
real-time assessment and documentation of studied construction process on the basis of 3D as-built models by using
a terrestrial laser scanner. Jaselskis et al. (2005) investigated the potential benefits of using laser scanning on
transportation projects, concluding that laser scanning can be very effective for the purpose of safe and accurate
construction measurement. Two innovative approaches of CAD object retrieval from range images for progress
tracking already exist (Bosche and Haas 2009, Golparvar-Fard et al. 2009). We expect that these approaches can be
quickly disseminated into industry practices during the course of this proposed research.
Automated operation process measurement, as opposed to human observation based process
measurement, has long been pursued in the context of video technologies (Bolivar and Mee 1997; Eldin and Egger
1990; Touran 1981), although recent research studies appear to favor the use of real-time radio frequency based
positional and/or identification sensors, such as GPS and RFID, for construction operation analysis (Hildreth et al.
2005; Su and Liu 2007; Navon and Sacks 2007). It is important to recognize that none of these methods can provide
the same rich information as videos can, but each of these methods rather complements each other. On the other
hand, in the absence of an efficient video interpretation method, tedious manual reviewing is required to extract
productivity information from recorded construction videos. Automated construction video analysis based on
computer vision methods has recently come into sight in the construction domain (Zou and Kim 2007; Kim and Bai
2009). Nevertheless, these studies only focus on particular computer vision algorithms in specific construction
operations such that results from these studies can be hardly extended into other operations. It is sufficient to
conclude that automated video data interpretation is still in its infancy in construction. But the recent quick
acceptance of wireless construction cameras in the construction industry has fueled a rising interest in confronting
the challenge of automated video interpretation. Taken together, real-time localization sensors and video
technologies provide means to monitor the dynamic process of construction operations. When such technologies are
coupled with intelligent data processing algorithms, resource utilization in an operation can be precisely measured in
a level that is close to human observation without the tediousness of manual approaches. Specifically in this
research, prototypes of Ultra Wideband (UWB) real-time location tracking methods (Teizer et al. 2007 and 2008)
and video intelligence methods (Bohn and Teizer 2009, Gong and Caldas 2009, Teizer and Vela 2009,) would be
used to form the basis of an operation process.
4. Recommended Path Forward
In essence, it is the information available for supporting progress tracking and process measurement and
the information these modules can produce that unifies the site sensing modules. More specifically, automated
progress tracking with 3D imaging or photogrammetric methods requires a CAD model (or Building Information
Model) of the current site coupled with schedule information as the base for progress evaluation. The as-built model
from automated progress tracking provides information of current site conditions. Meanwhile, the CAD model
provides projection about the facility under construction. Then, once sensing modules for operation process
measurement can be geospatially registered with these models, sensing data from these modules can be precisely
interpreted. This can be done by relating the spatial source of these data to the as-built and CAD models, which
contain rich project planning and execution information of all sorts of ongoing operations to be measured. In
addition, information of construction methods, which is essentially a recipe of the planned process of construction
operations, provides the ground for process measurement, resembling the connection between 3D CAD model and
progress tracking.
Onsite productivity measurement is a process of collaboration among multiple agents in a project team as
well as a process of fusing data from multiple information channels. The vision proposed here for automated site
sensing of productivity measurement is built upon a multi-sensory approach for measuring different aspects of
construction production and a collaborative schema for combining information from these sensors into productivity
data. The goal is to lay the foundation for scalable deployment of site sensing systems for productivity measurement
through creation and evaluation of a multi-sensory and collaborative site sensing infrastructure through a series of
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field experiments. For the 3-year period of activities recommended, limited and specific goals are: (1) Identification
of aspects to be measured in construction production and relating such aspects to different sensing modules; (2)
Investigation of (real-time) sensing technologies for their potential to measure various aspects of construction
productions in infrastructure delivery; (3) Development and evaluation of computational methods for interpreting
sensing data into productivity relevant information in different sensing modules; (4) Creation of mechanisms of
enabling data fusion from different sensing devices; (5) Formalization of procedures for synthesizing miscellaneous
information from a variety of sensors into final productivity information; (6) Creation and validation of new and
effective (real-time visualization) techniques to transfer generated knowledge among decision makers.
5. References
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