Enhanced Framework for Better Collection and Utilization of Daily Work Report Data

Transcription

1 Enhanced Framework for Better Collection and Utilization of Daily Work Report Data August 19, 2015 K. Joseph Shrestha PhD Student Department of Civil, Construction and Environmental Engineering Iowa State University H. David Jeong Associate Professor Department of Civil, Construction and Environmental Engineering Iowa State University Douglas D. Gransberg Professor Department of Civil, Construction and Environmental Engineering Iowa State University 1

2 Big Data Data is increasing at an alarming rate Variety: numeric, text, figures, images, audio Size: petabyte and zettabyte Velocity: periodic and real time Growth/complexity of data: advancement in digital infrastructure Collection Methods: smartphone, camera, voice recognition, navigation Storage Mechanisms: database, data warehouses, ontology frameworks Analytical Tools: KDD, machine learning and business intelligence Management Approaches: ERP, TQM, cloud computing 2

3 Digital Advancement 70 times faster computer 50 Million times higher storage capacity Processor speed growth (Gemsres.com 2006) 200,000 times growth in internet speed Eha (2013) Hard disk capacity growth (Jacob et al. 2010) 3

4 Current Trend in Industry Sectors Data is considered as Fundamental Asset New Oil New Science Frontier of decision-making is shifting drastically towards Data-Driven Insights Leading organizations are Competing on Analytics (Kennerly & Mason, 2008) $ 300 billion Potential annual value to US health care 60% - Potential increase in retailers operating margins 50% - Decrease in product development and assembly costs 4

5 Analytics correlates to performance MIT Sloan Management Review,

6 The Construction Industry One of the largest industries in the U.S. Collection and exchange of a large amount of data among project stakeholders* Very limited utilization of Construction Data Lagging behind other industry sectors in terms of utilization of technological intake and data utilization** * Cox, Sean, Jose Perdomo, and Walid Thabet Construction Field Data Inspection Using Pocket PC Technology. International Council For Research and Innovation in Building and Construction, Aarhus, Denmark. ** Holler, Jan, Vlasios Tsiatsis, Catherine Mulligan, Stefan Avesand, Stamatis Karnouskos, and David Boyle From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence. Academic Press. 6

7 The Construction Industry Productivity Growth (%) Data Utilization in US Industry Sectors ( Manyika et al. 2011) 7

8 Status of the construction industry 8

9 Highway Infrastructure Data SHAs invest a large amount of resources Roadway Inventory Preconstruction Service Daily Work Reports Pavement Condition Pavement Condition Data 8000 miles every 0.01 miles, 65 data fields 52 Million pieces of data annually 2.5 terabyte pieces of data Data collection: Every 2 years Cost - $600,000 ( Calvarese, 2007) 9

10 Data Information- Decisions 10

11 Daily Work Report (DWR) Data Site engineers collect a lot of data during construction called Daily Work Report (DWR) data Work activities being conducted everyday Amount of work completed everyday Labor and equipment hours Weather conditions Significant conversations with contractors Paper based and electronic DWR systems available for state DOTs 11

12 DWR Data Collection Electronic DWR Paper Based DWR 12

13 Daily Work Report (DWR) Data As much as 40% of the time of Site Engineers are spent collecting this data* There are multitude of applications of DWR data only few benefits are actually obtained DWR data is highly underutilized * McCullouch, Bob Automating Field Data Collection in Construction Organizations. In Construction Congress V@ smanaging Engineered Construction in Expanding Global Markets,

14 Objectives Review current DWR data collection and utilization practices Identify the possible benefits of DWR data Investigate the challenges for better utilization of DWR data Develop an enhanced framework to overcome challenges 14

15 Methodology Literature review Evolution of DWR systems Data collection and utilization practices Two nationwide questionnaire surveys DWR collection and utilization practices Challenges for better collection and utilization Develop framework Seven components Improving current practices Validate framework Seven DWR experts Ease advancement 15

16 DWR Data Collection Practice Safety and incident reports Non-compliance with contract terms Traffic control reports Significant conversation with contractors Pay-item quantities of work performed each day Site conditions (location, weather, etc.) Equipment usage details for each day Contractor s quality assurance tests Causes of Delays Agency s quality assurance tests Material certificates Contractor s progress schedule and reports Crew details for each day Payroll review Equipment usage details for each pay-item activity Crew details for each pay-item activity Duration of each pay-item activity Material delivery tickets Average rating Number of respondents Respondents collecting data in their digital DWR system Respondents collecting data in paper forms Respondents not collecting data Average rating of the importance of the data 16

17 DWR Analysis Automation Gap Percentage of users obtaining benefits Dispute resolution Progress monitoring As built information Identifying Project risks Activity cost estimation Contractor evaluation Production rate estimation Contract time determination Safety analysis Evaluating effects of the innovative contracting methods Average ratings Average automation rating Average importance rating Percentage of users who are getting the benefits 17

18 Data Use within State DOTs Gap Cost estimation team Auditing team Scheduling team Design team Accounting team Traffic safety team 7 20 Asset management team 3 15 Planning team Response count Used Yes Possible benefit Yes 18

19 Challenges Lack of analysis methodology Lack of automation Resources limitations Lack of training Interoperability issues 19

20 DWR Framework Data Model Interoperability Data Visualization DWR Framework Automation of DWR Analysis and Reporting Advanced Data Collection Systems Technical Aspects Business Practices 20

21 Automated Analysis Current DWR Systems Ideal DWR System Production rate estimation Contractor payment Progress monitoring Contract time determination As-built information Impacts of weather events Progress monitoring Contractor payment Application benefits Predictive analytics Text analytics of linguistic data Activity cost estimation Resources on construction site Dispute resolution Contractor evaluation Identifying project risks Asset management Work zone safety analysis Tracking legal requirements Public information Feedback system Quality control Evaluating effects of innovative contracting Claims and disputes 22

22 Production Rate Pattern Data Selection Preprocessed data Transformation Data mining Interpretation Knowledge DWR Data Dates, pay items, quantities, resources, sites conditions Cumulative quantities by time per unit resources Production rates for given site conditions Effect of site conditions Predict production rates Decisions 24

23 Data Model Current DWR Systems Proposed DWR Systems General information Equipment Mostly linguistic Systematic data attributes Work activities Labor No proper relationship between data attributes Data Model Proper relationship between data attributes Stockpile Weather information Civil right verification Classified comments 25

24 DWR Data Collection Systems Current DWR Systems Proposed DWR Systems Web based Paper based System types System types Mobile applications Desktop based Data collection Picture Portable devices Barcode Manual typing Data collection devices Advanced data collection devices GPS LiDAR Video RFID Equipment sensors 26

25 Other Components of Framework Interoperability Data Visualization Technical Aspects Mostly disconnected System integration Travelers' information system Bidding system Cost estimation system Planning system Business Practices Accounting system 27

26 Validation S.N. 1 2 Question This framework proposes some useful advancement over current DWR systems. This framework can aid in tackling current challenges (listed below) of getting benefits from current DWR systems: Average Rating Lack of proper data attributes Resources limitation Duplication of efforts Technical limitations Current business practices Current DWR systems can adapt some parts of the framework to improve their 8.0 existing system. 4 The framework can be adapted to develop a DWR system if a state DOT does 8.3 not have one. 5 The framework is comprehensive in terms of its scope The framework is easy to comprehend

27 Conclusions Need of data driven business practices Lack of utilization of DWR data Within state DOTs Only few benefits were obtained: many benefits were obtained by less than three-fourths Lack of automation Enhanced framework focused on Enabling automation Integration Reduce duplication of efforts Better utilization of data Data-driven decision making Future studies should focus on developing algorithms for automating the applications 29

28 Acknowledgement Funding Mid-America Transportation Center (MATC) and U.S. Department of Transportation (DOT) Research and Innovative Technology Administration (RITA) Valuable information State DOT representatives 30

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