This paper presents a model-based probabilistic structural identification that uses the deflection of prestressed concrete bridges (PSCBs) as observational information to perform Bayesian inference on the state variables associated with creep, structural rigidity, dead loads, shrinkage and prestress. The creep development is modeled as a stochastic process, and the structural rigidity is modeled as a stochastic field using the Karhunen–Loève transform. By incorporating the stochastic process/field into the inference frame, detailed information on structural states can be extracted. Considering the high dimension of the state variables, their posterior distributions are derived by the Hamiltonian Markov chain Monte Carlo (HMCMC) algorithm. As an illustrative example, two sets of deflection measurement of a case bridge are used to update the state variables in a sequential manner. Bayesian inference can calibrate the state variables, while the uncertainties associated with the state variables can be reduced. A K-means analysis can reveal the typical modes in the joint posterior distribution of the state variables, corresponding to the typical failure modes in the attributive analysis of the excessive deflection. The updated state variables are used in the probabilistic condition assessment associated with the deflection evolution.

This paper presents a Bayesian inference framework for updating the structural rigidity ratio of aging hollow slab RC bridges using deflection measurements. The framework models the structural rigidity ratio as a stochastic field along the hollow RC slabs, using the Karhunen–Loeve (KL) transform to capture spatial correlation and variation. Bayesian inference is then applied using deflection data from static loading tests, supported by a finite element model (FEM) and a Kriging surrogate model to enhance computational efficiency. The posterior distribution of the structural rigidity ratio is derived using a Markov chain Monte Carlo (MCMC) sampler. The proposed method was tested on an RC bridge with hollow slabs, using deflection measurements taken before and after reinforcement. The Bayesian updates indicated increased structural rigidity ratios after reinforcement, validating the effectiveness of the reinforcement. The deflection predictions from the updated models closely matched the measurements, with the 95% confidence bounds encompassing most of the data. This demonstrates the method’s validity and robustness in capturing the structural improvements post-reinforcement.

In this paper, the time-dependent properties of the elastic modulus of fly ash concrete under sustained compressive load were studied. An experiment was conducted and showed an increment of elastic modulus for two types of fly ash concrete (20% and 40% fly ash replacement) under sustained load. The mechanisms of this increment were analyzed, and two Representative Volume Elements (RVEs) were established to represent the micro-heterogeneous space of binder and concrete based on continuum mechanics. The shrinking core models of hydration and pozzolanic reaction were adopted to quantify the volume fraction of each phase within the binder RVE. A prediction model was proposed by incorporating the effects of extra hydration and time-dependent aggregate concentration rate under sustained load. Finally, parameter analysis including the influences of initial loading age and the loading level was conducted.

In this paper, the numerical micromodulus is derived for the plane stress problem to develop a new insight into the application of bond-based peridynamics. Considering the nonlocal property of peridynamics, the numerical micromodulus coupled with influence function provides a reasonable description of the long-range force effect. Through several numerical applications, the effectiveness of the numerical modulus coupled with various influence functions to simulate deformation and failure is analyzed. In addition, a load increment algorithm based on fictitious density is developed specifically for quasi-static problems. It is indicated that the introduction of the influence function can enhance the accuracy in deformation and failure simulation, which is valuable for the advancement and application of numerical micromoduli. Through a comprehensive trade-off between simulation accuracy and stability, the numerical micromodulus coupled with the exponential influence function proves to be the more effective option for brittle material.

1. The deterioration of reinforced concrete (RC) structures caused by reinforcing steel corrosion is a major challenge to the sustainability, condition assessment, and maintenance planning of urban...