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INSTRUCTORS: 
Anand Stephen, PE
Emani Majors
Armin Rashidi Nasab
Hazem Elzarka
Jeff Barghout

Purpose and Background

Technical presentations from the International Conference on Transportation & Development 2024:

Deploying Artificial Intelligence (AI) Assistants Towards Resilient and Sustainable Designs (15 minutes)

This presentation explores the integration of AI technologies within civil engineering workflows to enhance infrastructure resilience and sustainability. It demonstrates a proof of concept where engineers use AI tools to access environmental and design data directly from their CAD software, significantly reducing the time needed for preliminary analysis and site assessments. The session emphasizes the potential of AI in automating data retrieval from multiple sources, optimizing design processes, and improving decision-making to mitigate risks. Additionally, it addresses the challenges of bias in AI systems and the critical importance of maintaining traceability and quality control in AI-assisted engineering projects.

Predicting Ohio Bridges Conditions Using Multi-Target Machine Learning Algorithms (15 minutes)

This presentation details the development of an AI model aimed at predicting the conditions of bridge components—deck, superstructure, and substructure—across Ohio. The methodology involves using random forest and XGBoost algorithms to process and analyze data from the Ohio Department of Transportation, identifying key parameters that influence bridge health. The model achieves over 80% accuracy in classifying bridge conditions, demonstrating its reliability for real-world applications. This approach offers a significant improvement over traditional inspection methods by providing timely, data-driven insights for bridge maintenance and safety planning.

Utilizing Artificial Intelligence as a More Effective Approach to Infrastructure Assessment (30 minutes)

This presentation examines how AI can enhance the efficiency and accuracy of infrastructure evaluations. It highlights the use of advanced machine learning algorithms to analyze vast amounts of inspection data, identifying patterns and anomalies that might be missed by traditional methods. The session demonstrates AI’s capability to integrate data from diverse sources, providing comprehensive assessments of structural health and potential risks. This approach improves predictive maintenance strategies, reduces costs and enhances the longevity of infrastructure assets.

Learning Outcomes and Session Benefits

Upon completion of this course, you will be able to:

• Describe how Artificial Intelligence technologies are being integrated into civil engineering practices to improve efficiency, safety, and sustainability in infrastructure development and management.
• Identify the connection between AI and design tools, including current legal landscapes and ethical issues related to AI applications in infrastructure.
• Discuss the use of Multi-Target Machine Learning Algorithms in predicting bridge conditions, which enhances safety and enables more effective budget allocation.
• Explain how an innovative AI-driven roadway assessment tool provides accurate, cost-effective, and user-friendly data for evaluating pavement and infrastructure conditions.

Assessment of Learning Outcomes

Learning outcomes are assessed and achieved through passing a 10 multiple choice question post-test with at least a 70%.

Who Should Attend?

• Transportation engineers
• Transportation planners
• Traffic engineers
• Highway engineers
• Materials engineers
• Construction engineers

How to Earn Your PDHs and Receive Your Certificate of Completion

This course is worth 1 PDH. To receive your certificate of completion, you will need to attend the live session and/or watch the recording(s) and complete the post-session survey. If the course is taken On-Demand, there will also be a 10 multiple choice question post-test.

View Important Policies and System Requirements for this course