Automated Real-Time Tracking and 3D Visualization of Construction Equipment Operation Using Hybrid LiDAR System Chao Wang, Ph.D. Candidate; Advisor: Dr. Yong K. Cho, Associate Professor Robotics & Intelligent Construction Automation Lab (R.I.C.A.L), School of Civil and Environmental Engineering
1. Introduction
3. Field Tests and Results
PROBLEM STATEMENT: • Unstructured work areas like construction sites are difficult to graphically visualize because they involve highly unpredictable activities and change rapidly
• Low data collection speed and low object recognition rates. • Difficult to rapidly extract the target area from background scattered noises in a large and complex 3D point cloud.
RESEARCH OBJECTIVES: The main objective of this research is to design, develop, and validate a 3D visualization framework to collect and process dynamic spatial information rapidly at a cluttered construction job site for safe and effective construction equipment operations.
2. Methodology
Surface Modeling Process (a) Pre-scanned point cloud of static site environment; (b) (b) A crane model embedded with the site environment
Total processing time with different α value for the case study
4. Findings and Conclusions
Framework of the proposed method
This research proposes fundamental research on 3D workspace modeling to foster a breakthrough innovation in robotic manufacturing and automated construction site operations which will greatly benefit the future U.S. construction industry and society in general. The knowledge obtained will enrich the literature in the important area of construction engineering and management and provide solutions to industry-driven problems. This research also proposes vigorous goals for integrating research and education.
Data transmission network configuration
Monitoring Construction Progress at the Operation-Level using 4D BIM and Site Photologs Kevin Han (kookhan2@Illinois.edu), University of Illinois at Urbana-Champaign; Advisor: Mani Golparvar-Fard Limitations of Previous Research
Project: Student Dining and Residence Hall, Champaign, IL Images used courtesy of Turner Construction Co.
Unordered and uncalibrated daily construction site photos
4D as-built point cloud model automatically generated from daily site photos
4D point cloud models superimposed over 4D BIM generating D4AR models
BIM LOD and Schedule WBS may not be detailed
-
Material Classification Using small image patches
-
Z-buffer for dealing with static occlusions
-
Formalized knowledge of Construction Sequencing
-
Leveraging IFC-based BIM for reasoning in models with low LOD in BIM
For those elements with no visibility, progress is not reported.
IFC-based building information model is superimposed with the asbuilt point cloud model.
Automated as-built reconstruction with 160 photos collected along the site walk
Integrated & automated visualization of progress with photos, point cloud and BIM
FP and FN in monitoring
Point of Departure
• Model-based construction progress monitoring leveraging “relationship” and “flexibility” semantics from IFC
FN in Detection: Occlusion should be accounted for on:
FP in Detection: Formwork should not be detected as evidence of progress
Method for Inferring State of Progress based on Construction Material Classification
Results, Contributions, and Practical Significance Results per Image
Appearance-based Detection • Has potential for operation-level assessment • May work in low visibility situations (only requires 30x30 image patches)
Back-Projected BIM onto an Image Depth map of BIM model
Construction Material Library
Machine Learning Algorithm (Dimitrov & Golparvar-Fard 2014)
Confusion Matrix
Train
Sample Image Patches Infer Material
Machine Learning Algorithm ( HSV + LM)
HSV (3D)
Class #5: Concrete
Performance of the Proposed Algorithm:
LM Filters (48D)
Formwork: 100% Concrete: 90%
36 oriented filters at 6 orientations, 3 scales, and 2 filters & 8 derivative filters and 4 low-pass Gaussian filters.
Statistical Representation of Results: Appearance Frequency Diagram
Classified Material Types For Inspected Elements
Hue, saturation, & value (brightness)
K-Means Intensity Contrast and Color Cluster and Texton+HSV Normalize Filter Bank Form Texton Histograms Dictionary
Mat’l w/ Highest Freq. vs Second Highest Freq. Categorize & Label Material
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131
D4AR Web Viewer
Overall Workflow
Back-projected BIM onto an Image
D4AR Visualization
Frequency
Elements # Material with highest freq
Material with second highest freq.
Open Research Questions • Formalizing sequence of construction activities • Comprehensive “Construction” Material Datasets • Dealing with intra-class variability
Real-Time and Automated Monitoring and Control (RAAMAC) Lab
An Investigation of Occupant Energy Use Behavior and Interventions within a Residential Context Kyle Anderson and SangHyun Lee Civil and Environmental Engineering, University of Michigan
UM Dynamic Project Management Group Kyle Anderson (kyleand@umich.edu) Advisor: SangHyun Lee Civil & Environmental Engineering University of Michigan
Introduction
Research Framework
Energy Wastes in Residences
To date, innumerable efforts have focused on technological approaches to reduce building energy consumption; however, in the end, all buildings are operated by humans, and how humans decide to behave in them has a significant and meaningful effect on energy demand. Unfortunately, it is still not well understood how and why occupants choose to behave as they do in buildings, how behavior settings influence the spread of behavior, and what methods are effective at promoting durable improvements in occupant energy use behavior.
In order to complete the research objectives I have developed a twopronged research framework that integrates computer modeling and field experiments.
Unfortunately, studies to date using field-collected data have only investigated energy waste within commercial and/or non-domestic buildings and little is known regarding the quantity of energy waste in the residential sector.
Data Collection -Seven midrise dormitory buildings housing over 1,000 single and double occupancy rooms at Seoul Nation University in Seoul, South Korea. -Hourly individual occupancy data and hourly room electricity use from January 1, 2013 through December 31, 2013.
Objectives 1. Explore occupant behavior patterns in order to identify opportunities for improvement and barriers to change 2. Enhance our understanding of how contextual factor, especially social networks, influence the spread of energy use behavior 3. Test the durability of novel behavior intervention strategies and identify how and why residents alter their behavior
Exploring the Role of Social Networks in the Diffusion of Energy Use Behavior Agent-based modeling is used to model the diffusion of behavior using the theory of social influence [1]. These studies simulate deploying a normative behavior intervention (e.g., installing HEMS that provide comparative feedback to residents) in a building. The first study looks at network type, size, and density, while the second examines dynamicity. START
END
Check Moveout
YES
YES
NumOcc < Max
NO
t = t +1
NO
Dynamic? Variance in Outcome by Dynamicity
NO
7
Experiment Settings Connectivity (K) Social Network Size (N) Turnover Rate Occ. Energy Use
Study 1-1 Study 1-2 Study 2 2, 4, 6, 8, 10, 12 6 4 SFN, SWN, RND, REG SFN, SWN, RND, REG SWN 35 49 7, 35, 441 N/A N/A 32% annual All use mean 168 W and SD 123 W
Static
35
441
Dynamic
Network Size
Social Network
120 100 80 60
1. It was found that the average room consumed 30.2% of all electrical energy use (including plug loads, lighting, heating, and cooling) during periods of non-occupancy. 2. No meaningful relationship was found between total energy use and percentage of energy waste for individuals; high and low users had energy waste proportional to his/her total consumption.
Ongoing Research
40
120 100
Kruskal-Wallis p = 0.001186
Kruskal-Wallis p < 2.2e-16
20
YES
20 to Reach 40 Steady State 60 Behavior 80 Time
i < NumOcc?
80
i= 0
60
NO
40
Dynamic?
Mean SD in Energy Use Behavior (Watts)
t = t +1
NO
Influence & Energy Use Calculation
20
Steady State? Time = Max?
State Time -50to Reach Steady 0 50 Behavior100
Change in Energy Use by Dynamicity
YES
Social Network Generation
Main Energy Waste Conclusions
Change in Energy Use Behavior (Watts)
Occupant Initialization
Initialize New Occupant
7
35
Static
441
Dynamic
Network Size
Social Network
Main Modeling Conclusions 1. Social network structure can affect the time for interventions to take effect and increase uncertainty. 2. Dynamic social networks increase uncertainty in intervention outcome relative to static networks.
We are currently conducting a large scale field experiment at Seoul National University that is testing the effectiveness and durability of automated personalized normative feedback messages. Additionally, through multiple surveys we are gathering data on social network structure, the role of social networks in norm diffusion, identifying salient behavioral determinants, measuring spillover effects, and change in each of the aforementioned items over time. [1]
Friedkin, N. E. (2001). “Norm formation in social influence networks.” Social Networks, 23(3), 167–189.
Ex-Ante Simulation and Visualization of Sustainability Policies in Infrastructure Systems: A Hybrid Methodology for Modeling Agency-User-Asset Interactions Mostafa Batouli1, and Ali Mostafavi2 1PhD
Candidate, Civil and Environmental Engineering, Florida International University, Email: sbatouli@fiu.edu 2Assistant Professor, OHL School of Construction, Florida International University
PROBLEM STATEMENT
ILLUSTRATIVE EXAMPLE
BACKGROUND The existing methodologies do not consider: 1) Dynamic behaviors and interactions between user/asset/agency 2) Inherent uncertainties and complexities in analysis
RESEARCH OBJECTIVE Create and test an integrated framework for assessment of sustainability in Infrastructure systems
â&#x20AC;˘ A network comprised of 12 roads (Haas 2008) (Figure 3) Road Name
A B C D E F G H I J K L
Type
Pavement Type
Length (miles)
Width (Yards)
R I I I R R I R R I I I
Flex Com Flex Flex Flex JPCP JPCP JRCP JRCP Com Flex Flex
1.55 0.50 0.68 0.19 0.43 2.73 0.62 1.06 2.80 1.37 1.68 0.62
12.03 12.47 13.67 12.47 14.22 13.78 15.53 17.94 13.01 13.56 12.90 18.15
Construction Last STRN year Activity
1987 1962 1985 1960 1997 1991 1975 2000 1973 1973 1990 1960
1987 2006 1985 2006 2007 1991 2005 2002 2001 1999 2008 2004
ESAL/Day (in base year)
3.53 14.57 4.35 7.22 4.79 11.02 17.72 13.39 13.39 14.57 5.60 7.71
224 1185 1645 1756 864 688 1142 1785 1785 1185 1479 1756
Figure 5: Simulation of performance over 40 years PSR<3
Figure 3: Characteristics of the illustrative example
RESEARCH FRAMEWORK A System-of-Systems approach for base-level abstraction and multi-level aggregation of interrelations between agency/asset/user (Figure 1).
Economic Outcome
Policy Requirements
Year 5
Year 10
Year 15
Year 20
Year30
Year35
Year40
Modeling Asset Performance: đ?&#x2018;&#x192;đ?&#x2018;&#x2020;đ?&#x2018;&#x2026; = đ?&#x2018;&#x192;đ?&#x2018;&#x2020;đ?&#x2018;&#x2026;đ?&#x2018;&#x2013; â&#x2C6;&#x2019; đ??´. đ??š â&#x2C6;&#x2014; đ?&#x2018;&#x17D; â&#x2C6;&#x2014; đ?&#x2018;&#x2020;đ?&#x2018;&#x2021;đ?&#x2018;&#x2026; đ?&#x2018;? â&#x2C6;&#x2014; đ??´đ?&#x2018;&#x201D;đ?&#x2018;&#x2019; đ?&#x2018;? â&#x2C6;&#x2014; đ??śđ??¸đ?&#x2018;&#x2020;đ??´đ??żđ?&#x2018;&#x2018; Modeling User Behavior: Traffic growth depends on road performance Modeling Agency Behavior: Condition-based M/R strategy
SIMULATION MODEL
Year25
(2) Impacts of user behavior and budget Level on performance (Figure 7 and Figure 8) 3.8
A hybrid simulation model created to capture the dynamic behaviors of the system (Figures 4a and 4b)
Average Weighted PSR of Network
Social Benefit
Budget Limitations Demand/Pressure
Environmental Condition
Environmental Impact
Service
PSR=4.5
Figure 6: Visualization of performance at the network level
Budget Saturation 4.0
3.6
3.4
Tipping Points
3.2
3.0
3.5 3.0
2.5
Budget ($) 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 110,000 120,000 130,000 140,000 150,000
2.0 0
0 0 0 0 00 00 00 00 00 00 00 00 00 00 00 00 20 40 60 80 0 2 4 6 1 1 1 1 Available Budget
Figure 7: Impact of user behaviors on performance
Figure 1: Integrated framework for assessment of sustainability
4.5
Fixed TGR Behav ior A djusted TGR
Average Weighted PSR
â&#x20AC;˘ Problem: Deterioration of infrastructure systems (ASCE 2013) â&#x20AC;˘ Challenge: Sustainable restoring and improving (NAE 2008) â&#x20AC;˘ Solution: Policy and decision-making based on integrated assessment of infrastructure systems
1.5 0
10
20 Year
30
40
Figure 8: Impact of availability of budget on performance
SINGNIFICANCE
METHODOLOGY Use of a hybrid Agent-Based/System Dynamics simulation model based on three interrelated computational modules (Figure 2): Behavioral Assessment (Agent-Based Modeling)
Infrastructure Performance (System Dynamics)
Performance
Figure 4a: Class diagram of Figure 4b: Sequence diagram of the simulation model the simulation model
RESULTS Environmental Assessment (Adjusted LCA)
Figure 2: Hybrid AB-SD simulation model
(1) Simulation and visualization of the performance at the network level (Figure 5 and Figure 6):
(i) Simulation and visualization of the outcomes of policies (ii) Exploring of the emergent properties affecting the sustainability of infrastructure systems (iii) Fostering adaptive policies based on varying infrastructure characteristics, agency priorities, and user behaviors.
REFERENCE â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘
American Society of Civil Engineers (ASCE) (2013). Report Card for America's Infrastructure, Reston, VA, US. National Academy of Engineering (NAE) (2008). Grand Challenges for Engineering in the 21st Century, http://www.engineeringchallenges.org. (accessed December 15, 2013) Haas, Ralph. "The ICMPA7 investment analysis and communication challenge for road assets." Prepared for the 7th Int. Conf. on Managing Pavement Assets, Calgary. 2008.
Quantifying Human Mobility Perturbation
Qi Ryan Wang wangqi@vt.edu Advisor: Prof. John E. Taylor
Under the Influence of Tropical Cyclones BACKGROUND
RESULTS
• Tropical cyclones are becoming more frequent1,2 and intensified (2%-80%)3. Number of Trips
Frequency
• Hurricane Sandy: 202 deaths
(i) Travel
• Several events have caused significant fatalities, injuries, and damage in recent years.
Sandy (Cat. 3) in NYC r1=1-100m r2=100-500m r3=500m-1km
Oct. 29, 2012
• Typhoon Haiyan: 6000+ deaths
• How can human mobility perturbation be quantified?
Data Sample
Figure 1.
DATA COLLECTION
Oct. 29, 2012
Haiyan (Cat. 5) in Tacloban r1=1-100m r2=100-500m r3=500m-1km
r4=1-5km r5=5-10km r6=>10km
Other days
Average value from Oct. 30 to Nov.9, 2012
Nov. 5-7, 2013
Nov. 8-10, 2013
Average value from Nov. 11 to Nov. 28, 2013
Range of Δr
r4=1-5km r5=5-10km r6=>10km
Other days
Range of Δr
P(Δr )
Patterns
Oct. 29, 2012
Other days
Average value from Oct. 30 to Nov.9, 2012
Quantification
• Do tropical cyclones change human mobility patterns?
(ii) Movement
• Do tropical cyclones perturb human travel frequencies?
r1=1-100m r2=100-500m r3=500m-1km
Range of Δr
(iii) Perturb.
KEY QUESTIONS
Other days
Average value from Oct. 30 to Nov.9, 2012
• Typhoon Wipha: 39 deaths • Human mobility plays a key role in disaster evacuation, response and relief.
r4=1-5km r5=5-10km r6=>10km
Wipha (Cat. 4) in Tokyo
Δr(/m)
JSDS = 0.022 TS = ~24 hr
Oct. 29, 2012
Other days
Average value from Oct. 30 to Nov.9, 2012
Δr(/m)
JSDW = 0.063 TW = ~48 hr
Nov. 5-7, 2013
Nov. 8-10, 2013
Average value from Nov. 11 to Nov. 28, 2013
Other days
Δr(/m)
JSDH = 0.162 TH > 3 weeks
Figure 2. (i) Short-distance trips increased while long-distance trips reduced in Sandy and Wipha, but the changes are more chaotic during Haiyan. (ii) Movement data fit to truncated power-law distribution well for all three tropical cyclones; (iii) Quantifying both strength and duration of human mobility perturbation.
CONCLUSIONS • Tropical cyclones can cause human movement perturbation4,5. • Power-law still governs human mobility6,7 unless the storms are extremely powerful. • Quantification shows categories are associated with perturbation strength and duration. 4pm Oct. 29 to 4pm Oct. 30
4pm Oct. 29 to 4pm Oct. 30
REFERENCES: 1. Ferris, E., D. Petz, and C. Stark, The Year of Recurring Disaster: A Review of Natural Disasters in 2012, 2013, The Brokings Institution. 2. Landsea, C.W., et al., Impact of Duration Thresholds on Atlantic Tropical Cyclone Counts*. Journal of Climate, 2010. 23(10): p. 2508-2519. 3. Knutson, T.R., et al., Tropical cyclones and climate change. Nature Geoscience, 2010. 3(3): p. 157-163. 4. Bagrow, J.P., D. Wang, and A.-L. Barabasi, Collective response of human populations to large-scale emergencies. PloS one, 2011. 6(3): p. e17680. 5. Horanont, T., et al., Weather Effects on the Patterns of People's Everyday Activities: A Study Using GPS Traces of Mobile Phone Users. PloS one, 2013. 8(12): p. e81153. 6. Brockmann, D., L. Hufnagel, and T. Geisel, The scaling laws of human travel. Nature, 2006. 439(7075): p. 462-465. 7. Gonzalez, M.C., C.A. Hidalgo, and A.-L. Barabasi, Understanding individual human mobility patterns. Nature, 2008. 453(7196): p. 779-782.
Construction Workers’ Behavior Influenced by Social Norms:
A Study of Workers’ Behavior Using Agent-Based Simulation Integrated with Empirical Methods Seungjun Ahn (esjayahn@umich.edu) and SangHyun Lee Civil and Environmental Engineering, University of Michigan
Background Normative Message
Social Control
Objectives
• “Norm as motivational capital” • Limitation of individuals-focused, formal-rule centered approaches for improving worker behavior - Ineffectiveness - Development of adverse norms
• Research Topic: Absence Behavior - 24% productivity loss when absence rate is 6-10% - Absence behavior influenced by absence culture
• To enhance our understanding of the dynamic processes of social controls of workers’ absence behavior in construction by creating a formal behavior model • To extend our understanding of the group-level absence phenomenon in construction workgroups using computer simulation • To identify effective policies and interventions to reduce absenteeism by creating positive social norms in construction projects
Results
Methodology • Agent-Based Modeling and Simulation integrated with Empirical Research
• A. Agent-Based Modeling and Simulation - Based on Social Cognitive Theory of SelfRegulation (Bandura 1991) - Model parameters o Social Adaptation (S) o Formal Rule Adaptation (F) o Strictness of Self-Regulation (R)
• C. Model Testing - Individual-Level Behavior Rule Testing (Level 0): Logistic Regression Predictors of Worker Absence Level Perceived salience of social norms Perceived explicit social control
B -1.065 1.037
S.E. .314 .384
Sig. .001 .007
- Group-Level Behavior Testing (Level 1 & 2)
• B. Data Collection & Analysis - Survey data collected from a total of 228 construction workers at 3 different job sites
[L: Low, H:High, S:Social adaptation, F:Formal rule adaptation R:Strictness]
• Conclusions - Informal, social controls in work groups influence construction worker’s absence behavior despite the transient nature of employment in construction - Fostering a positive social norm in workgroups can be a cost-effective means of maintaining a low absence level in construction projects - Workers’ social adaptation and self-regulation play an importance role in the emergence of positive social norms
UM AEC
UM Dynamic Project Management Group