An automated data acquisition and data storage model for improving cost and schedule control

Cost and schedule control have been an important area of research. However, though a majority of construction projects employ some method of cost and schedule control, still, many projects suffer from ineffective control due to inefficient flow of information. Additionally, the problem is magnified by the difficulty in controlling data due to the limited time available to commit to this task and the lack of the proper organization, use, and management of data, coupled with inefficient manual data collection methods. Therefore, a solution to these problems imposes the challenges of integrating cost and schedule control and controlling the quality, integrity, and timeliness of data. To address these challenges, automated methods for acquiring, storing, and using the data in support of integrated control are needed. One mechanism to assist in effectively doing so is a database management system (DBMS). The difficulty, however, is that the proper organization, use, and management of data requires that it be well modeled. Unfortunately, engineering data models are almost universally developed in an ad hoc manner. Therefore, there is a fundamental need for using a formal, methodological approach to modeling data; in this case cost and schedule data.

Given the development, evaluation, and adoption of a formal method for structuring engineering databases, two additional problems remain which are incorporated into the scope of this dissertation. The first problem is that of populating the database with data. Such population must occur as efficiently and quickly as possible. Automated data acquisition capabilities, effectively coupled with the database in the domain of interest, must be explored. The second problem is that of using the data in the database. The database should contain data that directly and efficiently supports processing needs and engineering applications that are essential and appropriate in the domain of interest. The data collection mechanisms, the database structure, and the data must all satisfy specified needs. This dissertation suggests that one form of effective integration can be achieved by linking data collection, data representation, and data use in an automated fashion.