Seven beams, four columns, and two single-bay one-story frames have been recently designed in order to create different crack patterns and to investigate heat propagation in cracked reinforced concrete structures. Commercially available finite-element software is adopted to describe the thermal behavior of precracked reinforced concrete structures in fire, and to evaluate the effect of cracking. Both the experimental and numerical results showed that heat propagation tended to increase in the cracked regions compared with undamaged regions. The ultimate bearing capacity of structures in question in fire was calculated by means of engineering-oriented methods, by introducing or neglecting the effects that cracking has on the thermal field. In the former case, the agreement between the calculated bearing capacities and the test results was better. In the analysis of cracked reinforced concrete structures in fire, therefore, assuming a uniform temperature distribution in each longitudinal fiber and t...

For structural health monitoring (SHM), a complete dataset is crucial for further modal identification analysis and risk warning. Unfortunately, data loss can occur due to sensor failure, transmission system interruption, or hardware failure, which can lead to missing data. Therefore, this study proposes a bidirectional long short-term memory neural network (Bi-LSTM) response recovery method based on variational mode decomposition (VMD) and sparrow search algorithm (SSA) optimization that utilizes the structural response data between multiple sensors and can simultaneously consider temporal and spatial correlations. A dataset containing approximately half a month of monitoring data was collected from a certain project for training, validation, and testing. A publicly available dataset was also referenced to validate the proposed method in this paper. Using the public dataset, under 13 different data loss rates, the VMD + SSA + Bi-LSTM model reduced the RMSE of data reconstruction by an average of 65.01% and 45.35% compared to the Bi-LSTM model and the VMD + Bi-LSTM models, respectively, while the coefficient of determination increased by 62.21% and 11.19%. The data reconstruction method proposed in this paper can accurately reconstruct the variation trends of missing data without the manual optimization of hyperparameters, and the reconstruction results are close to the real data.

In order to obtain the degree of damage in reinforced concrete (RC) beams exposed to fire, using the equivalent fire exposure time as the damage index, a new method of damage identification based on the support vector machine technology was proposed. Firstly, the feasibility analysis was conducted based on finite element models of simply supported beams. Thereafter, four RC simply supported beams were designed for fire test and vibration test, which were used to amend the finite element model and the SVM-based identification method. Fire tests were carried out on 4 beams for 60, 90, 120, and 150 min, respectively. During and after the fire tests, structural modal information were recorded. The first two order modal information, as SVM input paraments, was used to predict the equivalent fire exposure time based on SVM. The predicted results were very close to the actual fire exposure time. The residual bearing capacities of the beams after fire were calculated according to the predicted fire exposure time, which were close to experimental results. It indicated that the equivalent fire exposure time as the output parameter for damage identification was reliable. Finally, on the basis of damage identification method for simply supported beams, a new three-step positioning method was established for identifing the degree of damage in continuous beams. The method was applied to a thress-span continuous beam. The numercial situlation results revealed that the three-step positioning method was accurate.