This research investigated the bond properties at high temperatures and with hybrid fiber addition between basalt fiber reinforced polymer (BFRP) bars and hybrid fiber-reinforced concrete (HFRC). The tensile strength and appearance morphology of BFRP bars were analyzed. Used scanning electron microscopy (SEM) to examine the microscopic morphology and used the bond-slip constitutive model to fit the bond-slip curves. When BFRP bars after exposed to 300 °C, their tensile strength dropped by 42%. With the increasing temperature, the bond strength and stiffness of normal concrete (NC) specimens decreased by 27.3 % and 67.5 %, respectively, while HFRC specimens decreased by 20.8 % and 55 %, respectively. Hybrid fibers increased the bond strength and stiffness of HFRC specimens by 27.1 % and 49.1 %, respectively. The best fitting models were the Malvar model and the Continuous Curve model.

Considering the critical role of uncertainties in structural damage detection, primarily arising from measurement errors and finite element model discrepancies, a nonprobabilistic approach based on interval analysis is proposed. This nonprobabilistic approach integrates phase space matrices with convolutional neural networks (CNNs) for damage identification. The compatibility of the phase space matrix data format with CNN allows for high sensitivity in detecting damage. Unlike probabilistic methods, this approach does not rely on specific probability distributions but considers the upper and lower bounds of uncertainties, making it highly applicable to real‐world applications. The proposed method employs the phase space matrix as the input for the CNN and the elemental stiffness parameter (ESP) as the output. When accounting for uncertainties, distinct networks are developed from the upper and lower bounds of the input phase space matrix. Both the undamaged state and the state under assessment are processed through these networks. The resulting outputs enable the computation of the possibility of damage existence (PoDE) and the damage measure index (DMI), which collectively provide a comprehensive assessment of the level and probability of damage. Validation using a numerical model and experimental data confirms the effectiveness of this method in accurately determining the location and level of damage while considering uncertainties.

Structural health monitoring (SHM) plays a pivotal role in ensuring the longevity and safety of concrete structures. Traditional methods for detecting cracks in concrete, while effective to a certain degree, suffer from limitations such as labor intensity, subjectivity, and the incapable to detect subsurface or minor cracks. This paper proposes a novel approach utilizing Bayesian neural networks (BNNs) to learn the relationship between concrete conductivity changes and crack width. The proposed approach takes account of the inherent uncertainties present in material properties and environmental conditions. Making use of the conductivity of fiber‐reinforced concrete, the proposed approach leverages the changes in electrical conductivity as an indicator of crack development. The BNNs model, integrating the predictive power of neural networks with the probabilistic insights of Bayesian inference, offers a significant advancement in crack width estimation. The proposed approach not only enhances the accuracy and reliability of crack detection with probabilistic output but also provides a robust framework for continuous monitoring of concrete structures, which is one of the key objectives in SHM. The results demonstrate the potential of using conductivity changes as a precise indicator for crack detection and the feasibility and accuracy of using BNN in addressing the challenges of uncertainty in monitoring data. This study contributes to the field by offering a probabilistic method for early crack detection under the uncertainty effect, enhancing the informed decision‐making by using SHM for structural maintenance.