A Visual Monitoring Framework for Integrated Productivity and Carbon Footprint Control of Construction Operations Arsalan Heydarian1 and Mani Golparvar-Fard2

1 Graduate Student, Vecellio Construction Engineering and Management Group, Charles E. Via Department of Civil and Environmental Engineering, and Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA; PH (540) 383-6422; FAX (540) 231-7532; email: aheydar@vt.edu 2 Assistant Professor, Vecellio Construction Engineering and Management Group, Charles E. Via Department of Civil and Environmental Engineering, and Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA; PH (540) 231-7255; FAX (540) 231-7532; email: golparvar@vt.edu

ABSTRACT As buildings and infrastructure are becoming more energy efficient, reducing and mitigating construction-phase carbon footprint and embodied carbon is getting more attention. Government agencies are forming incentive-based regulations on controlling these impacts and expressing control of carbon footprints as principle dynamic goals in projects. These regulations are placing requirements upon construction firms to find control techniques to minimize carbon footprint without affecting productivity of operations. Nevertheless, there is limited research on integrated real-time techniques to monitor operations productivity and carbon footprint. This paper proposes a new framework and presents preliminary data in which (1) construction operations are visually sensed through construction site imagery and video-streams; subsequently (2) equipment’s location and action are semantically analyzed through an integrated 3D image-based reconstruction and appearance-based recognition algorithm; (3) productivity and carbon footprint of construction operations are measured through a new machine learning approach; and finally (4) for each construction schedule activity, measured productivity and carbon footprint are visualized.

INTRODUCTION According to several research studies, the rise in Green House Gas (GHG) emission is very likely the main reason for most of the recently observed increase in the temperature and other climate changes (EPA 2010, IPCC 2007). On earth, GHG emissions from human activities have increased by 26% from 1990 to 2005 (EPA 2010). Over this period in U.S., GHG emission has increased by 14% (EPA 2010). Among these emissions, carbon dioxide which is the main reason for the rise in the temperature (EPA 2010) accounts for three quarter of the total GHG emissions, with increase of concentration by 31% over the same period of time; meanwhile, a rise of 35% is projected by the U.S. department of Energy (Artenian et al. 2010, IPCC 2008).

The construction industry is considered to be one of the major contributors of these GHG emissions (EPA 2010). According to EPA, historical emission from 14 industrial sectors in the U.S. count for 84% of the industrial GHG emissions, while the construction sector is responsible for 6% of the total U.S. industrial-related GHG

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emissions, placing the construction sector to be the producer of the third highest GHG emissions along all these sectors. Among all environmental impacts from construction processes (e.g., waste generation, energy consumption, resource depletion, etc.), emissions from construction equipment account for the largest share (more than 50%) of the total impact (Guggemos and Harvath 2006). Furthermore, embodied carbon - emissions from production and transportation of construction materials - accounts for another 8% of the global GHG emissions and is mainly released within the first year of any construction project.

In order to minimize concentrations of GHGs, the United Nations, many European countries, and the state of California are considering a reduction of 80% in GHG emissions by 2050, necessary to prevent the most catastrophic consequences of climate change (Kockelman et al. 2009, Luers 2007). Nonetheless in the U.S., a new set of EPA off-road diesel emissions regulations is rapidly becoming a concern for the construction industry (ENR 2010) and has required Associated General Contractors of America and the California Air Resources Board to postpone enforcements of these emission rules until 2014. Although these regulations are considered to minimize construction carbon footprint by a large factor, yet industry interest has been minimal due to high cost of the alternatives: (1) high cost of new equipment, and (2) upgrading older machinery. These regulations are challenging construction firms to find solutions to reduce the carbon footprint of their operations without affecting productivity and the final cost of their projects. In order to meet these ambitious reductions in carbon footprints, a major cut in GHG emissions due to construction operations, manufacture, and delivery of materials is necessary.

Among all decision alternatives, minimizing the idle time of construction equipment would result in reduction of fuel use, extension of engine life, and safer work environment for operators and workers on site. If the equipment is rented, reducing the idle time can reduce the rental fee and the cost associated with the labor. From a contractor’s perspective, better operation planning, deployment of equipment through a more accurate equipment idle time analysis will improve construction productivity, leading to significant time and cost saving (Zou and Kim 2007). Establishing and implementing idle time reduction policies enables the construction industry to take a proactive action in carbon footprint reduction (EPA 2010). Despite the importance, reducing idle time for any onsite operation requires proper assessment of productivity. It is important to first gather data on resources and processes that are used for each construction operation in order to measure and analyze productivity as well as carbon footprint.

Traditional data collection methods for productivity analysis (Oglesby et al. 1989) include direct manual observations; i.e., a set of methods that are adopted from stop motion analysis in industrial engineering, and survey based methods. Although this method provides beneficial solutions in terms of construction operations, but implementing it is time-consuming, manual and labor-intensive, and is prone to errors (Su and Liu 2007). This significant amount of information also affects the quality of the analysis, makes it subjective (Gong and Caldos 2009, Grau et al. 2009, Golparvar-Fard et al 2009) and therefore many critical decisions will be made based on these faulty or incomplete information, ultimately leading to project delays and cost overruns. Therefore, contractors only attempt to collect productivity data at the

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project information system level. Developing an automated productivity data collection method will allow the contractors to measure the process of operations throughout every stage of the construction, which is considered an important step towards on-site productivity improvement. Over the past few years, cheap and high resolution digital cameras, extensive data storage capacities, in addition to availability of internet on construction sites, have enabled capturing and sharing of construction image collections and video streams on a truly massive scale. This imagery is enabling construction firms to remotely and easily analyze progress, safety, quality, and productivity (Golparvar-Fard et al. 2010).

Systematic monitoring and control enables construction professionals, suppliers, and manufacturers to improve the operation’s productivity by assessing the carbon footprints. This may also result in motivating development of low carbon products and better planning for efficient operations. It seems imperative for the construction industry to (1) track construction operations and sense GHG emissions, (2) assess the carbon footprint of supply and manufacturing processes, (3) study the relationship between operations’ productivity and carbon footprints, and (4) visualize construction and supply chain carbon footprints. The proposed framework in this paper enables project stakeholders to visually determine the amount of carbon emissions in their projects and improve each activity by adjusting productivity and reducing idle time. Figure 1 presents an overview of the proposed method.

Figure 1. An overview of data and process in the proposed vision-based tracking and integrated productivity and carbon footprint assessment framework.

RESEARCH BACKGROUND In recent years, there have been a number of research groups that have focused on estimating, monitoring and controlling construction operation GHG emissions. Ahn et al. (2010) presents a model which estimates construction emission using a discrete event simulation. Peña-Mora et al. (2009) present a framework on integrated estimation and monitoring of GHG emission and recommend application of portable emissions measurement systems. Lewis et al. (2009a) presents the challenges associated with quantification of non-read construction vehicle emissions and proposes a new research agenda that specifically focuses on air pollution generated by construction vehicles. Lewis et al. (2009b) studies the impact of changing fuel

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type, and Tier 0, 1, and 2 engines and recommendations are made by development and practical application of emission inventories for construction fleet management. Artenian et al. (2010) demonstrated that lowering construction emissions could be achieved through an intelligent and optimized GIS route planning for the construction vehicles. Shiftehfar et al. (2010) also propose a visualization system which visualizes the impact of construction operation emissions with a tree metaphor. In Most recent study, Lewis et al. (2011) presents a framework for assessing the effects of equipment operational efficiency on the total pollutant emissions of construction equipment performing a construction operation. Nonetheless, data collection or analyses in most of these state-of-the-art approaches are not automated. Furthermore, there is a significant non-renewable energy which is consumed in the acquisition of raw construction materials, their processing, manufacturing, and transportation to the site which is not being considered in these approaches. An automated tracking system that can measure both construction operations and initial embodied carbon footprints could result in a faster and more accurate data collection technique.

Similarly in recent years, a number of research groups have focused on automated assessment of construction productivity and idle time. Gong and Caldas (2009), Grau et al. (2009), and Su and Liu (2007) all emphasize on the importance of a real-time construction operation tracking of resources. More specifically, Gong and Caldas (2009) presented a vision-based tracking model for monitoring a bucket in construction placement operations. Despite the effectiveness of the proposed approach, the operation equipment location and action are not simultaneously tracked. Zou and Kim (2007) has also presented an image-processing approach that automatically quantifies the idle time of hydraulic excavator; though this approach uses color information for detecting motion of equipment in 2D, and it since it uses color space, may not be robust to changes of scale, illumination, viewpoint and occlusions. To the best of the author’s understanding, there is no existing research on automated vision-based tracking that can simultaneously locate the equipment in a 3D and identify their idle times and actions. Such an approach not only allows productivity of construction operations to be remotely and inexpensively measured, but it also enables onsite monitoring of construction carbon footprint. Integrated with the initial embodied carbon enables construction practitioners to assess productivity and carbon footprint of their operations and decide on control actions that can maintain or maximize productivity, while the overall carbon footprint is minimized.

INTEGRATED PRODUCTIVITY & CARBON FOOTPRINT MONITORING The goal of the proposed framework is to establish guidelines on how to visually monitor construction equipment, increase productivity of operations, and reduce carbon footprint. To reach this goal, an initial study is done to understand time-cost-footprint relationship, equipment productivity, and construction resources. An automated and visual identification system to identify construction equipment’s location and action is developed; this tracking technique allows for performing a productivity analysis on each crew. To understand their relationship for every activity and operation, a side-by-side productivity and carbon footprint analysis was then performed. Hence, as an initial step an integrated 3D reconstruction and recognition algorithm is proposed to sense and model the construction site.

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In the proposed approach (1) construction operations are visually sensed through construction site video-streams from fixed cameras; subsequently (2) equipment’s are recognized and located in 2D frames. For this purpose (as observed in the process and data model presented in Figure 1), these videos are further processed to spatially recognize and locate equipment in 3D and go-register their location in D4AR, 4-dimensional augmented reality environment (Golparvar-Fard et al. 2010, 2009). Equipment actions are recognized using an action recognition model. Throughout this stage, for each equipment (i) the location Li(x,y,z,time) and action Action(Li) of construction equipment are monitored and reported. (3) productivity and carbon footprint of construction operations are measured through a new machine learning approach; finally (4) by integrating 4D Building Information Models for each construction schedule activity, measured productivity as well as operation and embodied carbon footprint are visualized. Figure 2 shows the IDEF-0 representation for monitoring of equipment actions, locations, and productivity.

Figure 2. IDEF-0 representation of tracking, analyzing location and action, measuring productivity and carbon footprint, and visualizing the results.

Productivity An accurate prediction of the productivity of construction equipment is necessary and critical in construction control. In this research, productivity of construction operation is estimated through a new action recognition machine learning approach. Through real-time action recognition model a process chart of construction equipment and its actions for a specific operation is produced.

Carbon Footprint Initial Embodied Carbon: To calculate an accurate construction emission rates, the proposed mathematical algorithm integrates the initial embodied carbon with operation carbon emissions (Eq. 1). The initial embodied carbon in building construction is from the non-renewable energy consumed in indirect energy use – energy for acquisition of raw materials, processing, manufacturing, direct energy use – transportation of the materials to the site, and the on-site construction and assembly use. Due to lack of accurate databases and tracking techniques of construction material resources, embodied carbon is usually deemed optional in carbon emissions analysis and calculations for construction processes. The proposed method is based on the D4AR (4D augmented reality) monitoring tool to query specific material used

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in every stage of the construction from the underlying building information model. Since the D4AR model is linked to the construction schedule, it can also provide a connection between embodied emissions and operations emissions.

Operation Carbon: To measure the operation carbon footprint, activities that need to be monitored are initially queried from the D4AR model. Similar to Lewis et al. (2011) and based on the monitoring component and manufacturing equipment dataset, for the action of each equipment, the engine power (EP), operation hours (OD), emission factor (EF), and load factor (LF), on-site humidity and site’s physical characteristics are measured (Eq. 2). The overall effect of humidity varies at different type of the day by 1% to 9%; for instance it is expected to have lower emission rates in the evening and early morning where the humidity level is higher and temperature lower (Lindhjem 2004). Figure 3 presents the instantaneous and accumulative carbon footprints and gained reductions. ∑# #

(1) (2) # (3)

where em is the Emission Module measurement of each action, and tm is the duration of each action. OE is the operations emission and EE is the embodied emission.

Figure 3. Construction Carbon Footprint

Concept Study The goal is to demonstrate the concepts of tracking, locating, and action recognition of the equipment. The operation includes one excavator and three dump trucks. The D4AR model is used to provide a 3D image-based reconstruction and BIM registration (Figure 4a). Once the entire site is reconstructed, using the vision-based tracking, equipment is tracked and located (4b, c). Locating, tracking, and identifying different motions of equipment at a given time for each operation, enables action recognition for deformable equipment body (4d). The actions for excavator included digging, hauling, dumping, swinging, and idle time; respectively, the recognized actions of each truck included moving, filling, dumping, and idle time. The 3D reconstructed scene and equipment locations are visualized in a Euclidean 3D environment. Once the location and action of equipment is recognized, an operation chart is created for one cycle (Figure 5). D4AR provides the material resources based on the schedule activity which allows for the calculation of embodied carbon. The operation emission can also be calculated using Eqs.1, 2, and 3. The overall

Instantaneous

Accumulative

ti

CFi

CFi

CF

ti

Accumulative

(a) (b) Instantaneous vs. Accumulative CF for an Operation

Instantaneous vs. Accumulative CF for all Operations

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instantaneous and accumulative emission rates are plotted (Figure 3). By comparing the operation sequence chart overall carbon footprint, the user can determine exactly how much carbon footprint is emitted for a given activity.

Figure 4. The integrating tracking and monitoring framework for an actual construction site (reconstruction based on 160 existing 2Mpixel images).

PERCIEVED APPLICATION This application simply allows the user to reconstruct a construction site and recognize the location and action of construction equipment. By recognizing the operational sequence, an automatic productivity analysis is performed. Meanwhile, carbon emission of the construction operations is calculated for each activity and is plotted to visually demonstrate the emission rate side by side with the productivity analysis. Compared to other sensing technologies (e.g., GPS, wireless trackers), this application is practical as it does not require “tag” construction entities. Considering the $900 billion construction industry, each 0.1% increase in efficiency can lead to $900 million in savings, resulting in a significant impact on the current construction practice and EPA regulations on construction GHG emissions.

Figure 5. Construction Operation Sequences

CONCLUSION With the new set of EPA regulations and current economy crisis, being able to reduce construction emissions, which is responsible for 6% of the total U.S. industrial-related GHG emissions, using the resources available without additional cost could be beneficial. This research focuses on the gained amount of reduction in carbon footprint through productivity improvements of different construction operations.

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One of the most challenging facts is measuring accurate operation productivity. To measure the productivity of the construction, paper proposes a new automated visual sensing technique, in which equipment’s location and action is semantically analyzed through an integrated 3D reconstruction and recognition algorithm using the D4AR model. Productivity of construction operation is then learned and estimated through a new machine learning algorithm. This joint assessment of productivity and carbon footprint for the first-time enables project managers to study their operations in real-time and revise their construction plan and operation strategies to simultaneously reduce their carbon footprint and increase/maintain the level of productivity.

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