A huge amount of information is generated for an infrastructure element/system during its lifecycle, from designs, specifications, plans, construction documents, process control plans, inventory management reports, cost estimates and control reports, schedules, and maintenance plans and records. As infrastructure designers, builders and owners adopt new computer technologies, computerized data are becoming more and more available. There exist numerous opportunities to exploit and extract knowledge from this vast amount of data. Unlike much of the previous research in machine learning that has been successfully applied in several domains, infrastructure management data are of multiple types, are generated from many different sources, and sometimes is of very low quality. The general direction of Dr. Soibelman research has been the development of frameworks, and algorithms, that support the acquisition, modeling, management, and analysis of such diverse infrastructure-oriented data.
Dr. Soibelman research concentrates on: (1) studying the increasing amount of available construction/infrastructure management data; (2) developing methods, processes, and tools for big data in Civil Engineering to generate novel knowledge from large construction/infrastructure management databases; and (3) better management of the large amount of available construction/infrastructure management data with the development of improved data acquisition systems (sensors, drones, cameras, lidar, etc.) and information retrieval methods by developing frameworks and technologies that combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization. By developing new technologies and by integrating and adapting existing ones he was able to derive new principles and methodologies that allow infrastructure and construction managers to better manage, and to extract concepts, causal relationships, and patterns of interest from complex infrastructure-oriented data.
Together with PhD students Dr. Soibelman developed the following data acquisition tools, systems, frameworks and methods (partial list):
- A framework for construction knowledge discovery.
- Methods for integrating unstructured text documents and images in A/E/C model based systems.
- A knowledge assisted search engine for A/E/C products.
- A decision support framework for electricity production vulnerability assessment.
- Automated defect detection for sewer pipeline Inspection and Condition Assessment.
- A geospatial clustering tool to support the detection of patterns in water distribution systems.
- Data driven techniques for ultrasonic monitoring of gas, oil, and water pipes.
- A learning system for electrical consumption of buildings.
- An Estimation Algorithm that leverages building information and sensing infrastructure for localization during emergencies.
- immersive virtual learning for workers-robot teamwork on construction sites.
- Civil Infrastructure Systems Open Knowledge Network
- Real time 3D reconstruction of war zones from drone images
- Urban Energy Efficiency modeling from infrared data acquired by drones.
- Data driven economic modeling tools for evaluating cost and impact of extreme events.