Typical deterministic and stochastic bridge deterioration modelling incorporating backward prediction model

Probabilistic Modelling of Marine Bridge Deterioration

Chloride induced corrosion of steel reinforcement is the main cause of deterioration of reinforced concrete marine structures. This paper investigates the relative performance of alternative repair options with respect to the deterioration of reinforced concrete bridge elements in marine environments. Focus is placed on the initiation phase of reinforcement corrosion. A laboratory study is desc...

Concrete Bridge Deterioration Prediction using Markov Chain Approach

Condition monitoring diagnosis of distress and forecasting deterioration, strengthening and rehabilitation of aging bridge structures is a challenge faced by many road authorities in the world. The accurate prediction of the future condition of bridge elements is essential for optimising the maintenance activities. Most authorities conduct regular condition inspection activities followed by a h...

Application of Artificial Intelligence for Bridge Deterioration Model

The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution ...

Deterministic modelling and stochastic simulation of biochemical pathways using MATLAB.

The analysis of complex biochemical networks is conducted in two popular conceptual frameworks for modelling. The deterministic approach requires the solution of ordinary differential equations (ODEs, reaction rate equations) with concentrations as continuous state variables. The stochastic approach involves the simulation of differential-difference equations (chemical master equations, CMEs) w...

Bridge Condition Modelling and Prediction Using Dynamic Bayesian Belief Networks