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A Smart Construction Object (SCO)-Enabled Proactive Data Management
System for Construction Equipment Management
Conference Paper · June 2017
DOI: 10.1061/9780784480830.017

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Niu, Y., Lu, W., Liu, D., Chen, K., & Xue, F. (2017). A Smart Construction Object (SCO)-Enabled Proactive Data
Management System for Construction Equipment Management. In Computing in Civil Engineering 2017 (Proceedings of ASCE International Workshop on Computing in Civil Engineering 2017) (pp. 130-138).

A Smart Construction Object (SCO)-Enabled Proactive Data Management System for Construction
Equipment Management
Yuhan Niu1; Weisheng Lu2; Diandian Liu3; Ke Chen4; and Fan Xue5
1

Ph.D. Candidate, iLab, Faculty of Architecture, Univ. of Hong Kong, Knowles Bldg, Hong
Kong. E-mail: yuhanniu@connect.hku.hk
2
Associate Professor, iLab, Faculty of Architecture, Univ. of Hong Kong, Knowles Bldg, Hong
Kong. E-mail: wilsonlu@hku.hk
3
Master of Philosophy Candidate, iLab, Faculty of Architecture, Univ. of Hong Kong, Knowles
Bldg, Hong Kong. E-mail: liudd@connect.hku.hk
4
Ph.D. Candidate, iLab, Faculty of Architecture, Univ. of Hong Kong, Knowles Bldg, Hong
Kong. E-mail: leochen@connect.hku.hk
5
Research Assistant Professor, iLab, Faculty of Architecture, Univ. of Hong Kong, Knowles
Bldg, Hong Kong. E-mail: xuef@hku.hk
Abstract
The importance of efficient construction equipment management can never be over emphasized.
However, in current practices, construction equipment management is reportedly inefficient. It is
largely based on rule-of-thumb rather than scientific evidence derived from solid data analytics.
It faces challenges such as ‘small’, erratic data. Building on previous studies on smart
construction objects (SCOs), this paper aims to develop a SCO-enabled proactive big data
management system to facilitate the data collection, data visualization and data analysis for
construction equipment management. It does so by first analyzing the problems of data
management in prevailing construction equipment management practices. Then, the architecture
of the proposed system is presented, which consists of two key components. The first one is a
highly customizable smart chip that integrates various sensing and communication modules for
proactively collecting and exchanging big data with volume, velocity, and verity inflowing from
daily equipment operations. The second one is the data analytics platform for data storage,
visualization and analytics. The pilot of the system has just been started in a central police station
renovation project in Hong Kong. Fruitful datasets and analytics are expected from the pilot
application of the proposed system. It is envisaged that the proposed system can supplement the
existing construction equipment management with more comprehensive and concurrent decisionmaking information.
Background
The importance of efficient construction equipment management can never be over-emphasized.
Construction projects range widely for developing the built environment of a city, including civil
works and high-rise buildings, and thus often involve plant-extensive activities such as
excavation, earth moving, rebar bending, concrete mixing, and hoisting. In the cost elements of
construction projects, the cost associated with construction equipment accounts for a significant
proportion. It has been reported that the equipment-related cost may range from 5% to 10% of
total cost for a building project, and could account for 40% or above the total cost for a civil
work (Siddharth et al., 2015. As the expenditures of construction projects are prodigious all
around the world (e.g., the Hong Kong government has projected public expenditure on capital
works to reach HK$70 billion for the fiscal year of 2016), a slight improvement in construction
equipment management represents a significant saving to project clients and contractors involved.

For construction equipment management, data management and proactive strategies derived
from dataset are becoming increasingly important for decision-making when big data has rapidly
become the “heart of the talk” of this era across a wide array of areas (Nirmala, 2015). The
accumulated data from machine operations, maintenance, and purchases/disposals has largely
been ignored before but suddenly it is uncovered like a gold mine, by mining which hidden
patterns, unknown correlations and other useful information can be extracted to provide better
prediction and decision-making. With the increasing promotion of data mining and data
management, rather than passively waiting for the data being generated, proactive data strategies
have been adopted to capture, store, process and analyze big data with a view to harnessing its
value. This is particularly opportune nowadays when data acquisition and communication
technologies such as Auto-ID, laser scanner, and sensor networks, and unit data processing cost
are becoming increasingly accessible.
Unfortunately, prevailing construction equipment management practices is highly dependent on
individual experiences instead of hard data and facts. The data on construction equipment
operations is recorded by contractors sporadically, if at all. The limited data is stored in different
cost centers such as a project site or a fleet, forming the so-called isolated “information islands”
that are further failed being communicated and known by other decision-makers outside an
“information island”. More reliable, comprehensive data sets that can cover the target population
to account for the totality of construction equipment operations are highly demanded by fleet or
project managers, or the top executive of a construction company. Furthermore, existing studies
on construction equipment management only use limited types of data (e.g., Vorster, 2005;
Edwards and Holt, 2009; Fan and Fan, 2015). Thus, it is urgent to improve construction
equipment management derived from reliable and comprehensive data analytics.
The paper aims to develop a SCO-enabled proactive data management system for construction
equipment management, with a view to informing better decision-making. The reminder of the
paper comprises four sections. Next section briefly introduces the current practice and the
limitations to manage the data related with construction equipment. Afterwards, by introducing
smart construction objects and the main aspect of construction equipment management, the
system architecture is proposed. Two major parts, the data collection part and the data analytics
part with their subsystems are explained in details. The methodology for the pilot of this system and
the current development status are presented in Section 4. The value and innovation of the proposed system
are briefly discussed in sections 5 and conclusions are drawn.
Current solutions and opportunities
Currently, data related to construction equipment operation (e.g. operation time) is written on
paper form, which is far from meaningful to derive some actionable information (e.g. breakdown prediction and efficient monitoring). As shown in Table 1, a possible solution to derive
more data sets would be hiring a site personal to record the operations on an ad-hoc basis. Hiring
someone to record the data from 7:00am to 7:00pm is not only tedious and error-prone, but also
extremely expensive. Statistics from Hong Kong Census and Statistics Department show that the
average daily wage of a general worker in June 2016 is around HK$945.7. This is particularly
financially burdensome given the fact that there is a myriad of equipment on a construction site
to be monitored. Installing a webcam to record the operation of all equipment would appear a
clever solution but video streams are not directly computable irrespective of some software

claiming to do so. Construction equipment giants such as Caterpillar® or Sony® would record
operation data in their recent equipment, e.g. their ready-mixed concrete vehicles are able to
record locations and daily volume of supplies. However, these data services are yet to be applied
to existing plants. Practitioners in the construction industry have also tried to use Auto-ID
technologies to collect operation data of equipment. However, scanning the Auto-IDs is highly
disruptive to ongoing construction processes. To sum up, the current methods are far from
satisfactory in collecting data on construction equipment operation. It is expected that data
related to all aspects of equipment operation can be collected in a more automatic and effective
manner.
Table 1 Existing solutions for collecting construction equipment operation data
Existing solutions
Manual recording
Webcam
Existing construction
equipment giants

Strength
Easy to learn and operate
Full operation recording
Tailor made, specialized system

Weakness
Tedious, error-prone, costly
Incomputable data
State-of-the-art and big
equipment only

Auto-ID

Accelerating data collection,
cheap tags

Manual scanning, interruptive

The SCO-enabled construction equipment management system
Smart construction objects (SCOs) are defined as ‘construction resources (e.g., machinery, tools,
device, materials, components, and even temporary or permanent structures) that are made
smart by augmenting them with sensing, processing, and communication abilities so that they
have autonomy and awareness, and can interact with the vicinity to enable better decision
making’ (Niu et al. 2015). In addition to some basic properties such as dimensions, materials,
and manufacturers, a SCO must have three core properties, namely, awareness,
communicativeness, and autonomy. Awareness denotes SCOs’ ability to sense and log their realtime condition and that of the surrounding environment; Communicativeness means the ability of
a SCO to output information it has obtained through its awareness; and Autonomy refers to the
ability of a SCO to take self-directed action or alert people for further action based on preset
rules. While these SCOs are still providing decision-making information to human decision
makers, what makes them different from conventional construction objects is that they can talk
to each other directly. In doing so, some routine or clearly rule-based decisions can be made by
SCOs autonomously without necessarily involving human decision makers in the loop (Niu et al.,
2015).
By making pre-fabricated components into SCOs, Niu et al. (2016) have proposed and tested a
SCO-enabled logistics and supply chain management system to facilitate decision-making,
which demonstrated potentials for achieving process and information concurrence. As a result,
more informed decisions could be made. Similarly, SCOs could be used to facilitate data
collection and informed decision-making in the construction equipment management while more
attentions should be paid to the volume and variety of data source. A SCO-enabled proactive
data management for construction equipment is thus proposed with additional considerations and
designs for data storage, data warehouse, and data visualization subsystems. Here, construction
equipment management may include, but not confined to, the aspects as elaborated in Table 2,
which is summarized from existing literature and demands from the construction practitioners.

To understand all these aspects, one needs to have reliable, comprehensive data collected from
real-life job sites and to analyze in a systematic manner.
Table 2. Main aspects of construction equipment management
Aspect
Equipment usage patterns
Efficiency and productivity
Fuel consumption
Carbon emission
Safe and authorized
operations of equipment
Regular or preventive
maintenance

Repair or replacement

Operating cost
Buy or lease
Depreciation and disposal
Fleet
configuration/optimization
Fleet tracking

Detail
To understand whether it is a seasonal, sloping stair, step up, spike, or
other pattern as a starting point for many other construction equipment
management inquires.
To understand whether the equipment is operating or idling by linking
to the job done.
Nominal fuel consumption and actual fuel consumption
Nominal carbon emission, and real-time actual measurement of
emission of carbon dioxide (CO2), nitrogen oxides (NOx),
hydrocarbon, to derive emission rates.
Auto-check equipment maintenance records, Personal protective
equipment (PPE), and qualification to operate the equipment.
Good maintenance will minimize maintenance and repair costs and
maximize production and profit, with the greatest impact on profits. It
helps control costs and service intervals, lengthens equipment life,
minimizes downtime and adds resale value. Regular inspections and
adhering to recommended maintenance schedules are the keys.
Preventive maintenance is to treat the problem proactively before it
becomes a catastrophic failure.
A major repair can change the depreciable equipment value due to an
extension in service life, while a minor repair is normal maintenance.
When it is not worthwhile to have a minor or major repair, replacement
is desired.
Fuel cost, tire lifecycle and cost, preventative maintenance, repairs,
operator wages, and other additional costs.
It is a classic dilemma in construction equipment management. Each
option has its own advantages and disadvantages, which are changing
from one period of time to another, and from one company to another.
It is not only about financial accounting manipulation, but also
strategic decisions relating to a company’s major asset.
Project managers reshuffle their equipment fleet on a regular basis for
resource allocation (Fan et al., 2008).
Modern construction equipment is tracked using GPS and other
locationing technologies for multiple purposes, e.g. security, usage
pattern analysis, efficiency analysis, etc.

In order to collected data from which the information in Table 1 can be possibly derived or
deducted, the system architecture of the SCO-enabled proactive data management system is
proposed as Figure 1. The system has two major parts, (A) SCOs for proactive data collection;
(B) data analytics for construction equipment management. For part A, existing construction
equipment are augmented into SCOs by a hardware end-point called i-Core. Encapsulating the
smartness of SCOs, i-Core denotes a standalone, programmable, and extendable integrated chip
that can be implanted to construction machinery, device, and components. i-Core turns deadweight construction equipment into SCOs using customizable sensors and communicating
modules. Sensors for capturing various types of data will be selected and tested for achieving the

functions of vibration detection, motion detection, real-time location detection and wireless data
transmission. With the sensors tested, selected and integrated, prototypes of i-Core will be
manufactured for lab testing and field trials. These prototypes will be attached, mounted or
embedded to the construction equipment in real-practice for data sensing and communications.
As displayed in the construction equipment layer, different types of construction equipment will
be incorporated into the system including the mobile plant such as mixer truck, static plant with
motions such as tower crane, and static plant such as scaffolding. Three series of i-Core are
developed. The location-focused series is mainly designed for locationing of mobile equipment.
Motion-focused i-Core is designed to monitor the frequently moving parts of equipment such as
the jib of the tower crane and cherry picker. Condition-focused series are designed for
monitoring the environmental factors that may have influence on the operation life of the
equipment. The SCOs with these customized i-Cores would form data network for possible data
exchange.
Part B consists of three major subsystems, which are the data warehouse subsystem to store data,
the data management subsystem to process data and the visualization subsystem to present the
data. Data captured by SCOs will be transferred into the data warehouse. These data serve as the
training data input for the built-in machine learning algorithm. The real-time status of the
equipment will be displayed through the 3D model animations in the user interface. These
animations can be replayed anytime when review of the equipment operation is needed.
Meanwhile, data about the fleet operation status such as moving, idling, and off-line will be
visualized in graphs and diagrams in the user interface as well. The analysis results, such as the
optimum usage rate, depreciation rate, carbon emission rate and other potential insights from the
data will also be presented in the user interface to facilitate prediction and decision making.
The data warehouse subsystem
The data warehouse subsystem is one of the key subsystems in this system, aiming to warehouse
various types of data from both i-Core networks and external data sources. Recent development
of open source and commercial big data software have successfully paved a way to store and
manage the gigantic amount of data observed from the complex actions and interactions of
construction equipment. In the proposed system, a set of libraries will be employed such as
Hadoop, Cassandra, Hive, and Spark with. The libraries will form the software infrastructure of
the data warehouse. In addition, conventional relational database such as MySQL and
lightweight formats such as JSON will also be incorporated.
The data management subsystem
The data management subsystem consists of a series of data operations on the data warehouse
through structured data pipelines (e.g. SQL), aiming to provide a toolkit of data management to
ensure the quality, consistency, and useful statistics for the data stored in the warehouse. Three
main modules are included in this subsystem, which are data cleansing module, data fusion
module, and applied statistics module. The data cleansing module is a software-based process for
data quality control, as a complementary process to the hardware controls. In comparison to the
hardware filters, the software data cleansing allows sophisticated rules and expressions which are
collected from the construction practitioners. The data fusion module aims to resolve
inconsistency of multi-faceted and multi-source data. In comparison to the noise data removal of

the data cleansing module, the data fusion aims to consolidate valid but different data. The aim
of the applied statistics module is to provide fundamental indices for data storage in the
warehouse. In this system, a set of value-added statistical analytics will be developed to index
equipment’s various representations, including the activities, efficiencies, the site safety and
behaviors, the maintenance and operational status, the environmental impacts, and the possible
risks of machine failures and operation failures. These indices can provide references for data
cleansing and data fusion, and also provide basic evidences to managers at construction
companies.

Figure 1. The architecture of the proposed system
The visualization and insights subsystem
The visualization and insights subsystem could help discover the patterns and value sunk in the
data ocean. Data visualization is a representation of data in a pictorial or graphical format. This
module aims to reorganize the data in some more intuitive ways. For managers, visualized data is
much convenient to uncover hidden patterns, unknown correlations and other useful information,
to provide better business prediction and decision making. For equipment operators, visualized

formats are also valuable in terms of safety (wider awareness, visualized semantic regions on
site), efficiency (knowing equipment’s physical status better, computer-aided planning), and
collaboration (awareness of other equipment and their needs). Some research and developmentfriendly visualization libraries such as D3.js will be incorporated to accelerate the development
of this deliverable.
The methodology of the system pilot
This study will also make good use of a “living lab” that has been established by the research
team over the past year with Hong Kong Housing Authority, local contractors and some crossborder suppliers. According to Bergvall-Kareborn and Stahlbrost (2009), a living lab represents a
user-centric research methodology for sensing, prototyping, validating and refining complex
solutions in multiple and evolving real life contexts. The living lab process, which integrates
both user-centered research and open innovation, is based on a maturity spiral concurrently
involving a multidisciplinary team in the following four main activities, including co-creation,
exploration, experimentation, and finally, evaluation.
An i-Core prototype has been developed to remotely control generator switch on site. When iCore is connected to the generator switch, managers/foremen could use text massage to remotely
control the generator switch. The bi-directional transmission between the i-Core and the data
analytics platform is achieved in the pilot test, serving as a solid base for other equipment.
Currently, the pilot of the SCO-enabled construction equipment management system just starts in
a central police station renovation project in Hong Kong, in collaboration with a big local
contractor in Hong Kong. In the initial stage, one i-Core is connected with an electricity switch
while a few i-Cores are expected to be installed for water pumps and tower cranes. A preliminary
data analytic system has also been set up based on the serves and databases in the ‘living lab’. It
is expected the data collected in the pilot could not only supply the management decisions but
also provide insights to improve the further development of the system. By comparing and crosschecking the data collected in the pilot with the desired data types derived from Table 2, the pilot
could help confirms the types of data needed for construction equipment management and
possibly identify new data types with decision-making implications.
Discussions and conclusion
Facing the fragmented and data deficient construction equipment management, this study
proposes a SCO-enabled proactive data management system for construction equipment
management that could proactively collect data and systematically analyze the data. By turning
the existing construction equipment into SCOs using i-Core, this system represents a novel
thinking that using SCOs will cause the least disruption to ongoing construction processes
instead of making radical changes to the existing construction operations. In fact, there are extant
studies focusing on how can innovations be accepted and well implemented (e.g. Rogers 1983;
Roumboutsos and Saussier 2014). A common agreement has been arrived that a technological
system of newness would be more easily accepted when the side-effects of such system on
system users become lower. The proposed system does so by augmenting the existing equipment
with sensing and communicating abilities without compensate their original functions and
appearances.

The system architecture and each of the subsystem are introduced in details in this study. An
ongoing pilot of the system is conducted in a ‘living lab’ following a co-production methodology.
In the initial pilot stage, i-Core is planned to be embedded to critical electricity switches,
generators, and tower cranes first. The setting-up is still in process. With the proposed system, all
data that might have cost implications, environment-protection implications, safety implications
and other possible use will be continuously collected by i-Core, while advanced big data
analytics will be applied to process and visualize the collected data. Unlike conventional
construction equipment management methods which are heavily relied on fragmented paperbased documents, the results generated by system-supported data analytics will be much more
comprehensive to support informed decision-making.
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