Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition

Software Defect Prediction Using Ensemble Learning Survey

Machine learning is a science that explores the building and study of algorithms that can learn from the data. Machine learning process is the union of statistics and artificial intelligence and is closely related to computational statistics. Machine learning takes decisions based on the qualities of the studied data using statistics and adding more advanced artificial intelligence heuristics a...

Concrete Bridge Deck Condition Assessment Using Robotic System Rabit

Development and implementation of RABIT (Robotics Assisted Bridge Inspection Tool) in condition assessment concrete bridge decks is described. The system uses multiple nondestructive evaluation (NDE) technologies in characterization of three most common deterioration types: rebar corrosion, delamination, and concrete degradation. The system implements four NDE technologies: electrical resistivi...

Detection of Bridge Deck

Delamination is probably the most serious form of deterioration I which is commonly found in britlge decks. It ultimately results in large scale spalling necessitating costly repairs. This type of failure is believed to be caused chiefly from salt induced corrosion of the reinforcing steel. An instrument designed to detect delamination, and tests conducted to val:iAate its perf'ormance, are .br...

Bridge Deck Deicing

Concrete bridge decks are prone to ice accumulation. The use of road salts and chemicals for deicing is cost effective but causes damage to concrete and corrosion of reinforcing steel in concrete bridge decks. This problem is a major concern to transportation officials and public works due to rapid degradation of existing concrete pavements and bridge decks. The use of insulation materials for ...

NEXTRANS Project No.173OSUY2.2 BAYESIAN UPDATING PROCEDURE FOR PREDICTION OF CONCRETE BRIDGE DECK CONDITION USING VISUAL INSPECTION DATA By