- Performance models for inspection planning
- Automated workflow for the Visualization of data quality and facility quality: edge quality map (subtopic: Workflow for defect detection: edge)
- Comparative analysis of sampling strategies and their interaction with geometries
Monday, June 22, 2009
Most important topics I want to focus on the next half year
Automatic Identification of Falsified Data Collected On-Site: Implications for Bridge Inspection and Construction Inspection
Erroneous data could be misleading for constructors, and could cause serious issues on construction sites.
Without a good methodology for identifying quality issues of the data, civil engineers have to accept whatever they have, and view all data with the equal importance.
However, the data have uncertainties and confidence level associated with them, hence, it would be good to have a method to automatically identify quality level of data, and visualize the uncertainty map of data sets.
This research targets this problem, possible related research: Jim Garrett Vaccum project.
Without a good methodology for identifying quality issues of the data, civil engineers have to accept whatever they have, and view all data with the equal importance.
However, the data have uncertainties and confidence level associated with them, hence, it would be good to have a method to automatically identify quality level of data, and visualize the uncertainty map of data sets.
This research targets this problem, possible related research: Jim Garrett Vaccum project.
Sunday, May 24, 2009
Automated As-Built Building Information Modeling: Human Computation: an Interactive Refinement Approach
(Warning: this essay is in a very rough form, I am still trying to make it more understandable, if you find the problem description paragraph is vague, please let know, I will try to improve it based on your comments, if you know any references related to this type of study, please let me know, I will be happy to share my reference library with you, thank you very much!)
The potential of Building Information Modeling (BIM) technology for various engineering applications has been recognized by many people from both industry and academia. These applications include: facility management, building performance simulation, construction scheduling, and many more. One critical issue to make these digital models of facilities is to make the information in it accurate and up-to-date. Since BIM comes from the domain of building design, it is very good at helping people to represent their design intent, and most BIM tools (e.g. Revit, ArchiCAD) provide automated supports for engineers to design new buildings and navigate through their virtual design models. However, these design models usually live in a perfect "virtual world", and might not necessarily reflect the actual status of "imperfect" buildings in the real world. Hence, according to what is actually constructed on the site, it is necessary to update the BIM design models to reflect their "as-built" status on the site.
A natural approach for capturing as-built conditions of constructed facilities is to utilize 3D imaging technology to capture detailed 3D geometry of these facilities, and reconstructe 3D as-built polygonal models of these facilities. Based on these 3D polygonal models, it is possible to use them as a starting point for reconstructing an as-built BIM. Generally, the process is: collect 3D data --> reconstruct 3D polygonal model in a CAD package such as AutoCAD --> export reconstructed CAD model into a BIM package such as Revit to create Object-Oriented BIM model. Many companies in the industry has adopted this workflow for reconstruct as-built BIMs or updating an as-designed BIM.
The issue associated with above mentioned workflow is that some part of this process requires human operators to conduct time-consuming and reprtitive manual operations. One such bottleneck lies in the process of reconstructing as-built BIM from CAD model: usually, engineers need to manually insert BIM objects to the model based on the geometric information in the as-built CAD model. First, inserting large number of objects manually is time-consuming. Second, inserting these objects at the precise location, and modifying their parameters (e.g. height and width of a window) to make the inserted object's geometry fit well with the as-built data are all challenging tasks. It happens that in order to speed up the modeling process, engineers tend to make decisions such as "windows on the same floor are on the same elevation", "walls on first floor and the wall on the second floor are coplanar", to simply copy windows, or extrude walls during BIM modeling. These assumptions might influence the fidelity of the reconstructed as-built BIM, since wall may not be coplanar, windows might not be well aligned. Making these assumptions actually causes loss of detailed information captured in 3D imaging data. However, it is very difficult to require engineers to manually confirm the fidelity of each objects they model, that will make such modeling process even more time-consuming.
One approach to combine the power of engineer and computer is an automated-refinement approach. Using their assumptions, engineers insert object at their rough locations, with approximately correct sizes. Even though these first guesses about object locations and dimensions might not be very accurate, it is helpful for an algorithm to identify 3D data close to these inserted objects, and modify the location and dimensions of these objects according to the 3D data points.
I call this process as a rough-modeling and automatic refinement process. Using this approach, engineers just need to insert objects at rough locations, and computer will do the rest to make the rough BIM fidel to the as-built 3D data. Without losing fidelity of the model, this approach should be able to substantially improve the productivity of as-built BIM reconstruction process, and make 3D imaging data more useful for stakeholders.
Critical issues need to be addressed is: 1) which assumptions can accelarate the modeling process while still keep the model good enough for a refinement algrithm? 2) which refinements should be conducted by refinement algorithms and how? location refinement? dimension refinement? what else? parameteric models should be adopted in this approach, but some issues might need to be further investigated.
The potential of Building Information Modeling (BIM) technology for various engineering applications has been recognized by many people from both industry and academia. These applications include: facility management, building performance simulation, construction scheduling, and many more. One critical issue to make these digital models of facilities is to make the information in it accurate and up-to-date. Since BIM comes from the domain of building design, it is very good at helping people to represent their design intent, and most BIM tools (e.g. Revit, ArchiCAD) provide automated supports for engineers to design new buildings and navigate through their virtual design models. However, these design models usually live in a perfect "virtual world", and might not necessarily reflect the actual status of "imperfect" buildings in the real world. Hence, according to what is actually constructed on the site, it is necessary to update the BIM design models to reflect their "as-built" status on the site.
A natural approach for capturing as-built conditions of constructed facilities is to utilize 3D imaging technology to capture detailed 3D geometry of these facilities, and reconstructe 3D as-built polygonal models of these facilities. Based on these 3D polygonal models, it is possible to use them as a starting point for reconstructing an as-built BIM. Generally, the process is: collect 3D data --> reconstruct 3D polygonal model in a CAD package such as AutoCAD --> export reconstructed CAD model into a BIM package such as Revit to create Object-Oriented BIM model. Many companies in the industry has adopted this workflow for reconstruct as-built BIMs or updating an as-designed BIM.
The issue associated with above mentioned workflow is that some part of this process requires human operators to conduct time-consuming and reprtitive manual operations. One such bottleneck lies in the process of reconstructing as-built BIM from CAD model: usually, engineers need to manually insert BIM objects to the model based on the geometric information in the as-built CAD model. First, inserting large number of objects manually is time-consuming. Second, inserting these objects at the precise location, and modifying their parameters (e.g. height and width of a window) to make the inserted object's geometry fit well with the as-built data are all challenging tasks. It happens that in order to speed up the modeling process, engineers tend to make decisions such as "windows on the same floor are on the same elevation", "walls on first floor and the wall on the second floor are coplanar", to simply copy windows, or extrude walls during BIM modeling. These assumptions might influence the fidelity of the reconstructed as-built BIM, since wall may not be coplanar, windows might not be well aligned. Making these assumptions actually causes loss of detailed information captured in 3D imaging data. However, it is very difficult to require engineers to manually confirm the fidelity of each objects they model, that will make such modeling process even more time-consuming.
One approach to combine the power of engineer and computer is an automated-refinement approach. Using their assumptions, engineers insert object at their rough locations, with approximately correct sizes. Even though these first guesses about object locations and dimensions might not be very accurate, it is helpful for an algorithm to identify 3D data close to these inserted objects, and modify the location and dimensions of these objects according to the 3D data points.
I call this process as a rough-modeling and automatic refinement process. Using this approach, engineers just need to insert objects at rough locations, and computer will do the rest to make the rough BIM fidel to the as-built 3D data. Without losing fidelity of the model, this approach should be able to substantially improve the productivity of as-built BIM reconstruction process, and make 3D imaging data more useful for stakeholders.
Critical issues need to be addressed is: 1) which assumptions can accelarate the modeling process while still keep the model good enough for a refinement algrithm? 2) which refinements should be conducted by refinement algorithms and how? location refinement? dimension refinement? what else? parameteric models should be adopted in this approach, but some issues might need to be further investigated.
Sunday, May 17, 2009
Collaborative Information Seeking for Construction Information Retrieval
During the construction process, constructors need to refer to large amounts of data and documents for making their daily decisions. These data and documents include job-site photos, construction schedule, on-site collected data about construction productivity, the history and plan of the crane operation. Most construction sites are dynamic and spatially complicated, hence these data tend to generate and be updated frequently, so that it might be challenging for constructors to seek useful information from these data to support their decisions, such as which parts of the constructed facility should be inspected in greater detail.
Due to the fragmental nature of the construction industry, the data and documents are scattered all places, and it is very difficult to organize them in a consistent manner despite the fact that many document organization development companies tried to do so (:::ref). Without a good organization, and since it is almost impossible to get such a uniform organization, constructors always rely on some search mechanisms to retrieve information from documents. Text-document search and image search algorithms are used by them to find useful information from large amounts of data and documents, that somehow address the problem of information retrieval for decision support in the building construction domain.
However, such ad-hoc approaches for retrieving construction related information might be subject to the search skills of the constructors, and it is likely that some constructors might not be able to extract all critical information related to their decisions. That might lead to construction accidents and late discovery of inconsistency of information, which can cause delays or failures of construction projects.
Some observation in some preliminary studies (::: references) indicate that rather than viewing information seeking as a single-user and single query sessions, there might be a potential to link these queries together, store these queries, and analyze these queries for future reference. This observation makes us believe that construction related information seeking do not have to be a single-person and single query process, and it is possible to enable collaborative search for more systematic, comprehensive, and efficient information seeking.
Recent progresses in the domain of collaborative search indicate that social network can be used to automatically suggest what queries might be useful for a specific user under a specific scenario. It has also been shown that the search history could be organized to tell people that under similar conditions what are the most critical information retrieved by previous people (including themselves). With such storage of correlations between queries of multiple people and multiple query sessions, it might be possible to systematically organize the information seeking "projects" for enabling more comprehensive information seeking for construction tasks and reducing problems caused by incomplete information seeking, and inefficient information retrieval.
Major advantages of such a collaborative and historic recording approach of construction information seeking could be: 1) systematic information organization, which might be more complete compared to single-query session approach; 2) the retrieved information can be labeled by people so that later they do not have to retrieve the same information again, which might be able to improve the information retrieval efficiency by saving the time for repeated searching sessions.
Due to the fragmental nature of the construction industry, the data and documents are scattered all places, and it is very difficult to organize them in a consistent manner despite the fact that many document organization development companies tried to do so (:::ref). Without a good organization, and since it is almost impossible to get such a uniform organization, constructors always rely on some search mechanisms to retrieve information from documents. Text-document search and image search algorithms are used by them to find useful information from large amounts of data and documents, that somehow address the problem of information retrieval for decision support in the building construction domain.
However, such ad-hoc approaches for retrieving construction related information might be subject to the search skills of the constructors, and it is likely that some constructors might not be able to extract all critical information related to their decisions. That might lead to construction accidents and late discovery of inconsistency of information, which can cause delays or failures of construction projects.
Some observation in some preliminary studies (::: references) indicate that rather than viewing information seeking as a single-user and single query sessions, there might be a potential to link these queries together, store these queries, and analyze these queries for future reference. This observation makes us believe that construction related information seeking do not have to be a single-person and single query process, and it is possible to enable collaborative search for more systematic, comprehensive, and efficient information seeking.
Recent progresses in the domain of collaborative search indicate that social network can be used to automatically suggest what queries might be useful for a specific user under a specific scenario. It has also been shown that the search history could be organized to tell people that under similar conditions what are the most critical information retrieved by previous people (including themselves). With such storage of correlations between queries of multiple people and multiple query sessions, it might be possible to systematically organize the information seeking "projects" for enabling more comprehensive information seeking for construction tasks and reducing problems caused by incomplete information seeking, and inefficient information retrieval.
Major advantages of such a collaborative and historic recording approach of construction information seeking could be: 1) systematic information organization, which might be more complete compared to single-query session approach; 2) the retrieved information can be labeled by people so that later they do not have to retrieve the same information again, which might be able to improve the information retrieval efficiency by saving the time for repeated searching sessions.
Planned topics for my research essays
- Google Trend as a Tool for Bridge Condition Assessment and Prediction
- Multi-Agent Localization
- Automatic Update of BIM Using Laser Scanned Data
- Collaborative Spatial Information Retrieval from BIM and 3D Data
- Interactive interpretation and visualization of the Data from National Bridge Inventory
- Object Recognition for Design As-built comparison
- Performance Model of Sensors and Algorithms for Augmenting Inspection Planning Algorithms
- Data Processing Workflow Learning: for More Intelligent Data Processing and Staff Training
- Simulation augmented original data retrieval for progress monitoring of bridges
- Vehicular Networks for Transportation Tracking
- Automated Workflows for Defect Detection of Constructed Facilities
- Data exchange standards for integrated representation of as-built and as-designed information
- Testbed for automated approach for as-built BIM reconstruction: from use cases to a standard evaluation approach
- The relationship between measurement sampling patterns and geometric shape's complexities
- QA/QC of 3D Data
- Comparative analysis of measurement sampling strategies and their relationship with shapes' characteristics
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