SITUATIONAL AWARENESS FOR THE BUILT ENVIRONMENT
In-situ Quality Control of Terrestrial Laser Scanner Data for Accurate Extraction of Architectural Building Features for the Scan-to-BIM Process
Laser scanners capture geometric information of a building in order of minutes and with millimeters accuracy, which makes them a valuable asset, especially when capturing small details of a geometry is required for reconstruction of as-built/as-is models in the Scan-to-BIM process. However, to date, challenges remain with accurate detection and extraction of building primitives from the scan data. These challenges often stem from the early data acquisition stage. Factors such as a poor scan plan, erroneous data collection, and presence of occlusions could lead to a poor quality scan data. Existence of data quality issues such as missing data or low point cloud resolution (point density) result in inaccurate 3D BIM models and often raises the need for redoing the scan process. The current industry practice tries to reduce the scan quality issues by performing redundant scanning from the built environment and executing basic on-site quality control procedures. These procedures include visual inspection of the scan data and comparison of the registration accuracy for overlapping segments of the scan. Although these procedures improve the scan quality, they are limited to the quality issues related to the scan registration accuracy and are often subjective to the surveyor’s judgment. This research focuses on analyzing the scan data of buildings, and specifically investigates the data quality requirements for modeling the architectural elements on the exterior of the buildings within the Scan-to-BIM context. An automated in-situ data quality control framework is designed that is formulated based on the specific requirements of the data processing stage of the Scan-to-BIM process and the expected level of detail (LOD) of the final BIM model. The general steps are summarized as follow: 1) The data quality issues and requirements specific to the Scan-to-BIM process are identified to develop a set of scan data quality metrics, 2) Semantic features from the scan data are derived with the aim to recognize the quality issues at the building level, 3) Object recognition methods are used to recognize the quality issues at architectural element level 4) The quality issues are traced back to their causing sources, and 5) Various alternative data acquisition means and methods are studied to improve the scan quality.
Framework for Automated Monitoring and Movement Analysis of Highway Retaining Walls Using 3D Laser Scanners
The architecture engineering and construction (AEC) industry has adopted innovative and novel alternative methods to ensure faster, more economical, and higher quality project delivery. Recently, there has been an increase in the use of laser scanners in the industry to create as-built models for accurate and rapid assessment of progress, productivity, and quality assurance. As part of this research, the data from an ongoing highway construction project are processed and analyzed. The project uses 3D laser scanners for regular monitoring of mechanically stabilized earth (MSE) walls that retain the soil supporting the highway alignment. In order to begin processing the point clouds, the accuracy of scans in terms of capturing all the geometrical features and details has to be verified. Additionally, the point clouds have to be cleaned from unwanted objects as well as noises. Once the point clouds are pre-processed, the vertical movements and lateral displacements of the walls are calculated by comparing point clouds from different dates. This research aims to fill the current research and practice gaps by 1- Defining an automated scan plan and data collection framework to minimize scanning and point cloud registration- related errors 2- Defining an automated point cloud cleaning and noise removal process 3- Improving the quality and efficiency of change detection and movement analysis using 3-D laser scanners.
Sponsor: Kiewit Corporation
PI: Prof. Burcin Becerik-Gerber
Spatiotemporal Visualization of Building Performance
Continuous maintenance, supervision, and monitoring of buildings require processing and analyzing large amounts of data generated by sources and archived in various databases. There is a need for building performance data to be visualized and presented for building managers to analyze large datasets concurrently and comparatively for timely and efficient decision making. To fulfill this need, the objectives of the research are to (1) examine information visualization methods for building performance patterns and trends through visual mining of data; and (2) investigate the hypothesis that building managers will have a more effective understating of building performance information, if provided with a spatiotemporal visualization of data in a 3D environment.
Indoor Localization for In-Building Emergencies
Building emergencies especially structure fires are big threats to the safety of building occupants and first responders. When emergencies occur, unfamiliar environments are difficult and dangerous for first responders to search and rescue, sometimes leading to secondary casualties. One way to reduce such hazards is to provide first responders with timely access to accurate location information. As part of NSF 1201198, this research assesses the value of location information through a card game, and identifies a set of requirements for indoor localization through a survey. The most important five requirements are: accuracy, ease of on-scene deployment, resistance to damages, computational speed, and device size and weight. This research introduces a radio frequency (RF) based indoor localization framework to satisfy these requirements. Two algorithms are designed to infer people's indoor locations based on RF signal data collected from existing sensing infrastructure in buildings or ad-hoc networks. Moreover, building information models are integrated to both algorithms. Building information plays an important role in mitigating multipath and fading effects in iterative location computation, enabling the metaheuristic based search for building-specific satisfactory beacon deployment plans, and providing a graphical interface for user interaction and result visualization. The framework is validated in extensive simulation and field tests, and has reported promising results in satisfying the aforementioned requirements for indoor localization at building emergency scenes.
USC i-LAB Research Video: Localization in Emergency
Acknowledgment and Disclaimer: This material is based upon work supported by the National Science Foundation under Grant No.1201198. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
As-Built Model Verification, Maintenance and Generation Using Image Capture Technology
As-built models and drawings are essential documents used during the operations and maintenance (O&M) of buildings for a variety of purposes including the management of facility spaces, equipment, and energy systems. These documents undergo continuous verification and updating procedures as buildings are changed and renovated overtime resulting in much time spent taking manual surveys of existing conditions and building dimensions. In some cases, as-built models and drawings don’t exist and need to be regenerated from as-built conditions. This project attempts to improve the efficiency of the as-built model creation, verification, and maintenance processes by introducing image capture technology to the facility management as-built workflow. As part of this research effort, building images are captured by ordinary digital cameras and then stitched together to create 3D point clouds. A case study of a university building is used to compare the time and costs associated with models generated from the image capture technology point clouds and technologies procedures currently employed by facilities management. The point clouds are also compared to existing as-built BIM models to assess their accuracy and their value for developing 3D as-built models for other buildings.
Sponsor: Autodesk, Inc
PI: Prof. Burcin Becerik-Gerber
Research Link: http://usa.autodesk.com/adsk/servlet/item?siteID=123112&id=16122418
RFID-Based Indoor Location Sensing Solutions for the Construction Industry.
Indoor location information is of great value to the construction industry laying the foundation of various context aware information services such as equipment maintenance and energy consumption monitoring. Among a number of competing technologies, Radio Frequency Identification (RFID) demonstrates an advantageous balance between system cost and accuracy, which makes it a promising solution for Indoor Location Sensing (ILS) in built environments. This project aims at developing an RFID based ILS system that is adaptable to the construction industry. An innovative localization algorithm has been developed and tested in room, floor and building levels. Its performance will be evaluated against the following criteria: accuracy, cost efficiency, robustness, scalability, and ease to use. Potential areas of application include facilities management, on-site construction inspection, building energy conservation and emergency response. In addition, the team is currently working on calibration and correction methods for algorithm improvement.
Sponsor: Lygnsoe Systems, Inc
PI: Prof. Burcin Becerik-Gerber
Development of a Spatial Analysis Framework to Predict Critical Segments of Water Distribution Networks
The main objective of this research is to investigate and develop spatial analysis and deterioration modeling methods in order to improve understanding of pipe breakage and the prediction of water pipe breaks and deterioration as phenomena in space. American water distribution systems are aging and more frequently presenting problems that are mostly visible as water pipe breaks. Water pipes breaks cause water supply interruption, damage to properties, and water quality issues and should be avoided. Also, as water distribution investment needs grow while available resources shrink, attention has been brought to the way water utilities use their resources to maintain and renew their systems. If the time until breakage of a pipe was known with certainty, maintenance could be planned in advance avoiding the problem that results from pipe breakage. Several studies have been done in order to understand how water systems and their components structural condition behave over time. However, understanding how pipe condition varies in space is also important because the factors that cause deterioration of pipes, such as soil characteristics and surface load, vary over space. Yet, few studies have explicitly targeted the spatial aspect of pipe deterioration.
Sponsor: National Science Foundation (Award number: 0825964)
PI: Prof. James Garrett
Co-PI: Prof. Lucio Soibelman
Acknowledgment and Disclaimer: This material is based upon work supported by the National Science Foundation under Grant No. 0825964. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
CAREER: Knowledge Discovery in Databases and Data Mining as New Tools to Support Research and Educational Advances in Modern Construction Management
The construction industry is seeing an explosive growth in its capabilities to both generate and collect data. Advances in scientific data collection, the introduction of bar codes for almost all-commercial products, new sensor technologies, wireless computing, and new laser scanning technologies, have generated a flood of data. These advances coupled with advances in data storage technology, such as faster, higher capacity, and cheaper storage devices, better database management systems, and data warehousing technology, have increased the availability of computerized construction data. This project intends to study this increasing amount of available data by applying data mining and knowledge discovery in databases. Knowledge discovery in databases and data mining are technologies that combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases and visualization to automatically extract concepts, concepts interrelationships, and patterns of interest from large databases. The objectives of this CAREER program are to: 1) Generate improved methods to obtain novel knowledge from large construction databases developing model-building templates and wizards to guide novice construction knowledge model builders through the process of creating models based on their own data; 2) Improve access to past construction management experience and knowledge by practitioners and students; 3) Use active learning techniques to improve education of students at all levels by developing an educational simulation game with the knowledge generated during this research; and 4) Teach civil and environmental engineering graduate students the process of knowledge generation through the application and development of data mining, machine learning and artificial intelligence tools.
Sponsor: National Science Foundation (Award number: 0093841 and 0630206)
PI: Prof. Lucio Soibelman
Acknowledgment and Disclaimer: This material is based upon work supported by the National Science Foundation under Grant No. 0093841 and 0630206. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Sensor-based Motorized Autonomous Responsive Target (SMART)
3D laser scanning technology is now widely and increasingly used to create as-built 3D CAD or Building Information Models (BIM) of existing facilities and new construction. Errors occurred during “Scan-to-BIM” process have different sources of error, including instrument calibration, environmental conditions, point cloud registration, fitting algorithms, and manual modeling. In addition, each of these processes is manual and time consuming. In the first phase of the SMART project, Sensor-based Motorized Autonomous Responsive Target (SMART) is devised to replace paddle targets to reduce registration errors and inefficiencies incurred by manual re-orientation of paddle targets. The second phase of the project focuses on automated meta-data transmittal between the SMART and point cloud engine so that no placement of target IDs by scan crew is necessary and registration process is automated.
Sponsor: Optira, Inc
PI: Prof. Burcin Becerik-Gerber
Student Driven Optimization of Physical Learning Environments
This project attempts to monitor physical learning environments at USC through real-time student input. A pilot survey was distributed to students enrolled in course in at least one of six preselected classrooms around the USC campus. The six classrooms were selected to reflect an accurate sampling of old and new construction as well as a wide range of technology capabilities. The survey was formatted to smart phones and computers with the intention that students would record their classroom experience while still mobile on the campus. One intended outcome of the study is a better understanding of any impact that environmental comfort attributes (i.e. lighting, temperature, ventilation, air quality, acoustic), and physical space attributes (i.e. size, furnishing, audio/visual equipment, layout, connectivity) have on students and how effectively they learn. The results of the survey will be used to better tailor the classroom environment to student needs. Another purpose of the study is to compare the student environmental experience to the projected environmental experience monitored by USC Facilities Management. The data gathered through the survey will be compared to actual facility data captured by the temperature, lighting, and air handling systems of the buildings. Discrepancies between student experience and facility data will be analyzed in an attempt to improve facility monitoring. In the second phase of this project, the input from students will be stored and visualized in a database that is a 3D interface for physical space visualization. A blog, twitter or similar type of application will be used for information aggregation and instantaneous feedback and sharing.
Collaborators:Technology Enhanced Learning
PI: Prof. Burcin Becerik-Gerber
Knowledge Management and Visualization in Support of Vulnerability Assessment of Electricity Production
With the rapid growth in demand of electricity, vulnerability assessment of electricity production and its availability has become essential to our economy, national defense, and quality of life. The main focus to date has generally been on protecting power plants and energy transmission systems. However, the extraction and delivery of fuels is also a critical component of the value chain for electricity production. A disruption at any point in the infrastructure could result in lost power production and delivery. The need for better analysis of fuel delivery vulnerabilities is pressing. Therefore, the purpose of this paper is to present the preliminary results of a research project that aims to analyze the vulnerability associated with delivery of fuels and to ensure availability of fuel supplies, by providing insight into likely vulnerability problems so that solutions and preventative methods may be devised. In this research project, a framework for electricity production vulnerability assessment was proposed. Different data sources were integrated into a data warehouse to allow interactive analysis of enormous historical datasets for coal transactions and coal transportation. By summarizing and slicing the historical datasets into different data cubes, the enormous datasets were able to be analyzed and visualized. An interactive GIS interface allows users to interact with it to perform different queries and then visualize the results. The analyses help decision makers understand the impact of fuel delivery disruption and the vulnerabilities in the coal transportation system. Thus, solutions and policies might be advised to avoid disruptions.
Sponsor: Department of Energy(DOE) NETL
PI: Prof. Lucio Soibelman
Computer Vision and Image Reasoning for Automatic Defect Detection: A Case Study on Sewage Pipes Inspection
To regularly and proactively assess conditions of sewer infrastructure systems to ensure their structural integrity and continuity of services, it is critical to advance the state of automated pipeline inspection and condition assessment. Currently, a critical issue is to address realistic defect detection that deals with real sewer inspection data. This research project seeks to develop a framework to enable automated detection of defects in sewer pipelines from inspection videos and images. The need for and the challenges of automated defect detection in sewer infrastructure condition monitoring were studied. Based on a general detection and recognition model established in this research, a change detection based approach which is tailored to solve the challenges in this sewer pipeline domain was developed and tested on several case study.
Sponsor: RedZone Robotics
PI: Prof. Lucio Soibelman