Civil Infrastructure Systems Management Lab
Civil Infrastructure Systems Management lab focus on the study of mathematical models for the management of civil infrastructure systems. Our research covers a wide range of issues in the engineering and management of civil infrastructure network, such as modeling the deterioration of civil infrastructure asset (e.g., pavement, bridge, and railroad), developing optimization models for the planning of Maintenance and Rehabilitation (M&R) activities on infrastructure network, analysis of transportation system performance, funding needs analysis for infrastructure preservation, and application of advanced information systems for infrastructure asset management.
Assistant Professor
Dr. Lu Gao
Background:
- Ph.D. Civil Engineering, The University of Texas at Austin, 2011
- M.S. Civil Engineering, The University of Texas at Austin, 2007
- B.E. Civil Engineering, Tsinghua University, 2005
Dr. Lu Gao is an Assistant Professor at the Construction Management Department. He also holds other responsibilites such as the Manager of the Transportation Infrastructure Management System Lab and Coordinator of the Construction Management Graduate Program. Dr. Gao has completed his Ph.D. in Civil Engineering at the University of Texas, Austin. His research research interests include Civil infrastructure management, Transportation system analysis, Pavement maintenance and rehabilitation and Construction safety.
Graduate Research Assistants
Mehrdad Fonooni
Background:
- Post-M.S. Architectural Technologies, Southern California Institute of Architecture, LA, California
- M.S. Architecture. AZAD University, (CTB), Tehran, Iran
Mehrdad Fonooni is a Graduate Research Assistant at the Civil Infrastructure Management Lab. He has more than 8 years of experience working in the fields of Architecture, Construction and Project Management cooperating with prominent consulting firms. His research interests include BIM, Smart Construction Safety, Integrated Project Delivery and Sustainable Development.
Wilson Ren
Background:
- B.S. Petroleum Engineering, University of Alberta, Canada
Current Research:
- Project Delivery Systems
Wilson Ren is a Research Assistant at the Civil Infrastructure Management Lab. He has completed his Bachelors of Science in Petroleum Engineering at University of Alberta. He has worked as a directional drilling engineer and assistant rig manager at both Canada and Trinidad & Tobago for three years. His research interests include Commercial Vehicle Tracking for Cargo Safety and Seal Coat Application.
Satish Mamidi
Background:
- B.Tech. Civil Engineering, University College of Engineering, Osmania University, Hyderabad, India
Current Research:
- Project Delivery Systems
Satish Mamidi is a Research Assistant at the Civil Infrastructure Management Lab. He has completed his Bachelors of Science in Civil Engineering at University College of Engineering, Osmania University. He is currently working on the Project delivery systems for infrastructure. He has a work experience of 3 years in the Civil Industry. His research interests include Urban Development, Transportation Systems and Project Delivery Systems.
Likhitha Boyina
Background:
-
B.Arch, SRM University, 2015
Likhitha Boyina is a Research Assistant at the Civil Infrastructure Management Lab. She has completed her Bachelor’s in Architecture from SRM University and is currently pursuing her Master’s in Construction Management. She has a work experience of 1 Year as an Architect at an Architectural firm.Her research interests include Urban Developoment, Green Infratructure, BIM, Infrastructure Management and Transportation Systems.
Previous Members
Daisy Saldarriaga
Background:
- B.S. Civil Engineering, Universidad de Cartagena, 2010
Daisy Saldarriaga was a Research Assistant at the Civil Infrastructure Management Lab. She has completed her Bachelors of Science in Civil Engineering at Universidad de Cartagena, 2010. Her research work included Synthesis of Service Life Prediction for Bridges in Texas. She has a work experience of 3 years in the Civil Industry. Her research interests include Infrastructure Management, Transportation Systems and Pavement Management.
Vahid Dehkordi
Background:
- M.S. Transportation Engineering, K.N.Toosi University of Technology, 2011
- B.S. Mechanical Machinery, Shahrekord University, 2008
Vahid Dehkordi was a Research Assistant at the Civil Infrastructure Management Lab and is currently pursuing his Master’s in Construction Management. He has completed his Bachelor’s in Mechanical Machinery at Shahrekord University and Master’s in Transportation Engineering at K.N.Toosi University of Technology. His research interests include Mathematical Modeling and Optimization, Highway Engineering and Transportation Analysis.
Omkar Dhatrak
Background:
-
B.S., Civil Engineering, Pune University, 2014
Omkar Dhatrak was a Research Assistant in Civil Infrastructure Management Lab. He graduated in December 2016 majoring in Construction Management.He has completed his Bachelor’s in Civil Engineering at Pune University. Omkar’s research work included Pavement Maintenance and Rehabilitation Optimization considering Spatial Dependency.
Tejus Prasad
Background:
- B. Arch, ANRV School of Architecture, 2014
Tejus Prasad was a Research Assistant in Civil Infrastructure Management Lab. He graduated in Dececmber 2016 majoring in Construction Management.He has completed his Bachelor’s in Architecture at ARNV School of Architecture. Tejus’s research interests include Optimization, Deterioration Modeling, Transportation Infrastructural Performance and Modeling, Pavement Management Systems.
Funded Research Projects
Transportation and Economic Impact of Short Line Railroads
Many shortline railroads service communities that only have one significant industry and if that industry fails, the entire community would suffer economically as a result. Increasing truck traffic would both increase roadway maintenance requirements and potentially reduce safety of others using roadways where Class III rail service is lost or not available. This study seek to identify direct and indirect customer bases of the railroads, conduct interviews with short line railroad officials and their customers, and follow up with interviews of local community leaders (i.e. mayors, economic development directors, Rural Rail Transportation District (RRTD) officials, etc.) to determine detailed roles and plans for the short line network of the state.
Service Life Prediction for Bridges
In procurement requirements for design-build contracts, the Texas Department of Transportation (TxDOT) may implement a 100 year service life requirement for bridge structures. However, there is no indicated measures for how this can be achieved. In addition, for design-bid-build and design-build projects, TxDOT and consultants use TxDOT recommendations for durability to improve service performance. But no quantitative methods or codified guidance is available to demonstrate how the enhanced service life requirements are met. Moreover, because of the large number of existing old bridge, the evaluation of the remaining service life of these bridges is a very important economic problem for TxDOT. As the replacement of all these bridges far exceed the available financial resources, it is important to prioritize the repair works based on the estimated remaining service life. This study will (1) Identify the state of the art report on service life prediction for bridges beyond 75 years, (2) Report developing quantitative criteria and identified deterioration methods, suitable for including in a procurement document or manual to allow demonstration of meeting service life of 100 years, and (3) Document tests or inspections with appropriate thresholds for determining achievable service life at handoff (e.g., 5 years, 25 years, 50 years) when maintenance responsibility is transferred from a design-build developer to a government owner.
Investigating the Security of TxDOT’s Traffic Signal Systems
The safety critical nature of traffic infrastructure requires that it be secure against network-based and physical attacks, but this face a strong challenge. Modern traffic signal systems have evolved from series of standalone pieces of technologies coordinated through synchronized time clocks to a series of sophisticated programs running on a series of connected computers operating networked together using both wireline and wireless technologies. While new technologies have greatly enhanced how agencies design and operate their traffic signal systems, it has also increased the exposure of agencies to a new cyber security threat. This research will identify network and physical vulnerabilities in existing practices TxDOT’s traffic signal systems based on general practices used by agencies to secure their traffic signal system infrastructure, and outside/inside attack process, tools and technology. This project will develop a methodology for assessing the network and physical security. This methodology will be tested in five municipalities of different sizes and capabilities for the vulnerability and potential consequences of different types of attacks on traffic signal systems network. The research team will summarize the assessment and finalize recommendations. The recommendations and attack data collection approaches will also be evaluated in testbed, so the team’s recommendations will guarantee to offer security for TxDOT’s traffic signal systems and infrastructure.
Study of Short Term Skid Improvements by Light Texturing With a Milling Machine
Skid problems on roads can result from flushing and bleeding where excessive road-mix binder can accumulate on a road’s surface as well as general wear of the surface by traffic. This may result in a polished surface that may increase the chances of accidents due to the reduced skid resistance. Strategies to address this problem include mill and fill or overlay rehabilitation but another cost effective solution is to remove only the top portion of the surface course using light-texturing or micro-milling. Using this process a pavement milling machine can remove as little as 3/8 inches off the surface. Unlike a typical mill-and-fill operation, there is not an additional step in laying a new wearing course after milling has been completed. Instead, the newly exposed surface will already have the desired final texture and noise properties, and can be opened to traffic sooner. Some Texas districts have already implemented this technology and have achieved substantially improved skid resistance and reduced rutting with no detrimental effects to the existing pavement. However, these texturing improvements have not been studied to determine how well they improve skid and how long that skid improvement lasts. Therefore, there is an immediate need for conducting a study that can assist TxDOT districts better understand the effect of light-texturing in improving the skid resistance and also to identify the best practice of employing this technology. The objective of this research is optimize the light-texturing process by evaluating variables such as the number of teeth on drum, speed of the milling machine, the depth of milling, cutting pattern, pavement type, type of aggregate, climatic zone, traffic loading, etc. Pre- and post-milled sections will be skid tested to investigate these influence variables. Based on a thorough statistical analysis of the data, recommendations and guidelines towards improving light-texturing procedures will be prepared.
Manage the Transportation System
In this research, we propose an integrated approach to better manage the Texas transportation system under limited funding, where a multi-tier management system will be established. Resources will be allocated among tiers according to their level of service and performance goals. Moreover, a user fee backed public finance mechanism is also part of the proposed system. In addition, we will also propose an optimization method to address infrastructure deterioration and mobility problems together. With optimization process, given a set of infrastructure maintenance and mobility improvement projects, the proposed method will select the optimal combination of projects under limited funding by maximizing the overall system performance. The proposed idea is an integrated approach to identifying the proper funding strategy and level of maintenance of existing infrastructure through the implementation of integrated maintenance, operations, and finance aiming at maintaining Texas’s economic competitiveness and support Texas’s sustainable economic growth.
Machine Learning- Based Text Mining of Construction Job Market
The construction industry is the nation’s largest sector employing more people than any other industry. According to the Associated General Contractors of America (AGC), construction jobs surged up by 17,000 in October 2012. AGC also mentions that Houston is the number one metropolitan that offers maximum employment from the construction sector – 12,300 new construction jobs were added to the workforce in the past one year. With the current economy recovering slowly, it is important for university educators to prepare and equip students for any job opportunities (e.g., from construction industry) of today and tomorrow with the necessary skill sets. To effectively do so, it is essential to understand the job requirements in greater detail including skills, qualifications and experiences.
The fast growing online job search engines make it possible to apply a new approach to understand the construction job market and employer expectations. Job search engines like “Indeed.com”, “Monster.com”, “Simplyhired.com” and “Careerbuilder.com” provide millions of job openings with daily updates. Also, they provide detailed information on qualifications and special requirements for each job opening. A preliminary search by the researchers found that Indeed.com posted around 100,000 construction related job openings in the past three months. The main objective of this research is to utilize the huge amount of online construction job posts and extract information that provides us with the requirements to face the demands of the industry. The developed methodology would also help educational institutions to compare their curriculum to the industry and hence bring changes if necessary. A two-step approach will be implemented by the researchers. The first step is to find job advertisements in bulk and extract the job requirements from the web page (100,000 job posts have already been downloaded and more are expected to be collected in this research). Machine learning method will be employed to parse the ad texts from job advertisements and extract job skill terms. More specifically, Bayesian classification method will be implemented in learning extraction patterns for the domain of job requirements downloaded from the Web. Once the job requirements for each job opening are extracted, the second step is to use text mining technique to analyze the frequency of each specific skill, qualification and experience. This analysis will be carried out for each job types in the construction industry (e.g., cost estimator, scheduler, etc.). At the end of this research, a clear job definition along with the required skill sets for that job will be obtained. This result can be used as a focus to improve the curriculum of the construction program in universities. The methodology developed in this research can be applied to any other industries and the corresponding curriculum in an educational institute.