A Knowledge Engineering Approach to Analysis and Evaluation of Vertical Construction Schedules
{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}
{'SignBlockName': 'William Hakala', 'PO_EMAI': None, 'PO_PHON': None}
Computerized project management systems are widely acknowledged                 as deficient planning aids.  Specifically, their inability to                   collect and interpret qualitative and subjective project data                   requires construction managers to interpret such information                    largely unassisted.  Research in the field of Artificial                        Intelligence, which has been proven to be successful in many                    other fields, holds promise for providing such assistance in                    construction schedule analysis.                                                                                                                                 The proposed knowledge engineering research project will extract,               formalized, articulate (1) empirical and judgmental knowledge                   about construction network analysis and (2) traditional project                 management theory to develop a prototype intelligent assistant.                 This research advances the state-of-the-art by formalizing an                   amorphous mass of construction knowledge and by automating some                 of the tasks that senior project managers now know how to do                    well.                                                                                                                                                           The methodology utilized is stepwise simple and aimed at first                  and foremost extracting knowledge.  The methodology also                        dispenses measureable milestones to provide a way to verify                     progess.  While the results of this research will indicate that                 many of routine, and tedious operations can be automated, the                   principal goal is to establish that knowledge-based decision-                   support systems can allow project managers to become pro-active                 rather than re-active.                                                                                                                                          The investigator has a strong background in project planning and                control and the research team is geared to conduct the work.  The               budget is appropriate and the institutional resources are                       appropriate.                                                                                                                                                    An award is recommended for the first year of a two-year grant.
{'FirstName': 'C. William', 'LastName': 'Ibbs', 'PI_MID_INIT': None, 'PI_SUFX_NAME': 'Jr.', 'PI_FULL_NAME': 'C. William Ibbs', 'EmailAddress': None, 'PI_PHON': None, 'NSF_ID': '000208351', 'StartDate': '02/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Illinois at Urbana-Champaign', 'CityName': 'URBANA', 'ZipCode': '618013620', 'PhoneNumber': '2173332187', 'StreetAddress': '506 S WRIGHT ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_ORG': 'IL13', 'ORG_UEI_NUM': 'Y8CWNJRCNN91', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF ILLINOIS', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '1442', 'Text': 'CONSTRUCTION AND INFRASTRUCTUR'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1986&All=true', 'xml': '8613298.xml'}