Shri Bhidé

Betti, Raimondo

Bridge Deck Condition Rating Forecast—Machine Learning Models

Heng Liu, Ph.D.; Jean A. Nehme, Ph.D., P.E.; and Ping Lu, Ph.D., P.E.

1. Background Information

Machine learning models are developed to forecast bridge conditions using deep learning algorithms. Current model development focuses on bridge decks. In...

Bridge Deck Condition Rating Forecast—Survival-Based Models

Raka Goyal, Ph.D., P.E.

Introduction

The survival-based models on Federal Highway Administration (FHWA) InfoBridge^TM (FHWA, 2020) are probabilistic bridge deterioration models based on a methodology that combines...

Bridge Deck Condition Rating Forecast—Base Models

Base models are deterministic statistical models that are easy to understand and implement. With base models, historical time duration for each deck condition rating of selected types of bridges is calculated from training subsets of the National Bridge Inventory (NBI) (Federal Highway...

RABIT™ Bridge Deck Assessment Tool

In the United States, the stewardship and management of approximately...

Long-Term Bridge Performance (LTBP) Program Publications

Overall Program Presentation 2

Program Objectives

Overall Program Presentation 1

FHWA Long-Term Bridge Performance Program

Designated in the "SAFETEA-LU" surface transportation authorization legislation as a 20-year research effort to improve our knowledge of bridge performance

Funding is currently only authorized through FY-2009

...

Power Point Presentation II

Primary Tasks and Responsibilities

Matrix Diagram. Development and Pilot Tasks and Responsibilities. The diagram depicts a matrix with the two sub matrices; one representing the development phase and the other representing the pilot program. Both sub matrices have common...