Automated Spatial Data Analysis and

Information Modeling for Construction and

Infrastructure Management

Pingbo Tang, Ph. D.

Assistant Professor

Del E. Webb School of Construction

Adaptive Intelligent Materials & Systems Center

Ira A. Fulton Schools of Engineering

Arizona State University

• Construction management

– Construction quality control

– Construction progress monitoring

• Facility management

– Facility maintenance

– Renovation design

– Asset Management

• Infrastructure management

– Bridge inspection

– Bridge health monitoring

– Transportation safety analysis

Spatial Data in the AEC Domain

AIMS Center Open House, May 17th, 2013

• Manual spatial data collection, processing and interpretation– Data collection: Manually decide when and where to collect

data, use what kind of instruments (camera? Total station? Laser scanner?), and how to configure them (resolution? data collection rate?)

– Data processing: Manually extract geometric primitives and texture information from the data, obtain geometric attributes of geometric primitives

– Data interpretation: Recognize objects, calculate particular attributes related to the status of objects, and derive information for decision makers (spatial-temporal clashes, etc.)

Bottlenecks and Motivations

AIMS Center Open House, May 17th, 2013

• Enable automatic data-quality based planning of data

collection activities

– Quantitative evaluation of the performances of spatial data

collection instruments

• Which factors influence the data quality?

– Develop planning algorithms automatically identify most suitable

sensors, appropriate sensor locations and configurations for

achieving the best data quality satisfying domain requirements

– Example: test-bed for laser scanners

Data Collection

AIMS Center Open House, May 17th, 2013

Data Processing: Automatic Measurements

Define and Execute Workflows on Point Clouds for

Extracting Surveying Goals

Define a work flow using a template (or based on predefined workflows)

Execute the workflow and report results

User-defined work flows

Point cloud

A user-defined workflow

AIMS Center Open House, May 17th, 2013

• Workflows for deriving and visualizing spatial information

contained in spatial data

Data Interpretation

Locations hit by trucks

AIMS Center Open House, May 17th, 2013

Data Interpretation: Deviation Analysis

AIMS Center Open House, May 17th, 2013

0.100m ~ 0.120m

0.080m ~ 0.100m

0.200m ~ 0.220m

>0.220m

0.180m ~ 0.200m

Position change

BIM

Point cloud

Data Interpretation: Change Analysis

AIMS Center Open House, May 17th, 2013

Data Interpretation: Community

Connectivity

As defined by USGBC,

“community connectivity”

emphasizes that new buildings

are preferably to be constructed

in developed site with multiple

types of services (e.g., banks,

schools, and restaurants) ready

in walking distances (0.5 mile),

so that occupants of new

buildings can have better

access to various existing

services (USGBC, 2009).

AIMS Center Open House, May 17th, 2013

• Geospatial analysis for identifying critical factors highly

correlated with the bridge condition ratings

Data Interpretation: Bridge Map

Superstructure Rating: 0~9

Factor/Item

NumberDescription

ITEM36A Traffic Safety Features - Bridge Railings

ITEM43A Structure Type - Material and/or Design

ITEM27 Year Built

ITEM41 Structure Open, Posted, or Closed to Traffic

ITEM31 Design Load

ITEM91 Designated Inspection Frequancy

ITEM43B

Structure Type - Type of Design and/or

Construction

ITEM36C Traffic Safety Features - Approach Guardrails

ITEM64 Operating Rating (tons)

ITEM65 Method Used to Determine Inventory RatingAIMS Center Open House, May 17th, 2013

Data Interpretation• Abnormal operation cycles of boring process

AIMS Center Open House, May 17th, 2013

B1 B2

B3

MH 27.3 MH 26.102

MH 26.101

MH 26.100 MH 26.99B

MH 26.106 B4

Abnormal cycles increase the average duration of

cycles by more than 100%!!

• Spatial change analysis for construction productivity analysis, safety

management, and quality control

• Quality assessment and quality control (QA/QC) of Building Information

Model (BIM) and spatial data

• Geospatial correlation analysis of National Bridge Inventory (NBI) database

• Remote sensing for bridge health monitoring and scour evaluation

• Sustainability analysis of transportation systems

• VANET for real-time spatial information distribution to avoid potential

transportation risks and jams

Other Current Efforts

AIMS Center Open House, May 17th, 2013