As a no-disturbance integrated-retrofitting technique, an external rocking frame was widely used on reinforced concrete (RC) structures. Yet, with the increasing demand for seismic strengthening of existing buildings, it has become a concern to evaluate the seismic strengthening schemes based on seismic resilience. Firstly, the dynamic equation of the structural system was derived, and the deformation control mechanism was revealed; thus, the corresponding design method was put forward for the rocking frame reinforcement. Secondly, after soft-first-floor structures were reinforced by rocking frames, the evaluation method of the reinforcement scheme was investigated based on seismic resilience. Finally, the feasibility of the assessment method was verified by a soft-first-floor frame structure, and a comparison was made between the method proposed in this paper and the conventional method. The results find that the soft-first-floor structure reinforced by the rocking frame increased by 10% in the inter-layer displacement and improved by 55.6% and 63.0% in the injury and mortality rates, compared to the buckling-restrained brace scheme. This indicates that the reinforcement scheme of soft layer structures with rocking frames is feasible and effective, and the reinforcement evaluation method proposed in this paper can quantitatively reflect the improvement in seismic performance.

The highly selective synthesis of 4- tert-butyltoluene from toluene with tert-butyl alcohol over zeolite catalysts was performed in the liquid phase. The order of activity for alkylation over zeolite catalysts was as follows: H-BEA > H-Y > H-MOR > H-ZSM-5. The main reaction products observed were 4- tert-butyltoluene and 3- tert-butyltoluene. 2- tert-Butyltoluene was completely absent in the product. A kinetic study of the reaction of toluene with tert-butyl alcohol over pure H-BEA (Si/Al = 25) was performed. Experimental runs were conducted at 160, 170, 180 and 190 °C and reaction times of up to 240 min. Based on the results obtained, a simplified kinetic model was developed. The kinetics could be described by the pseudo first-order model.

To investigate the pitting evolution behavior of carbon steel in industrial–coastal atmospheric environments, a multiphysics numerical model was established by coupling tertiary current distribution, mass transport, and deforming geometry based on electrochemical corrosion mechanisms. Based on this framework, the effects of pH, salt concentration, and relative humidity (RH) on the pitting corrosion rate were systematically analyzed, and a predictive model for the pitting rate was subsequently developed. The results indicate that the pitting morphology gradually evolves from an initial conical shape to a hemispherical morphology and eventually develops into a quasi-cylindrical form. When the pH value increases from 6.0 to 7.0, the pitting rate decreases significantly, with a maximum reduction of 23.96%. Within the salt concentration range of 0.5%–1% (mass fraction), increasing salt concentration markedly accelerates the pitting process, resulting in a maximum increase in the pitting rate of 10%. In addition, the pitting rate exhibits a pronounced peak within the RH range of 72%–77%. The corrosion rates predicted by the proposed model are in good agreement with the experimental results, with relative errors within 5%. These results indicate that the numerical model and prediction formula established in the present study can reasonably predict the pitting evolution behavior and corrosion rate of carbon steel.

Visible corrosion on coated steel bridge surfaces is manifested through irregular changes in color, texture, apparent roughness, and boundary morphology. RGB inspection images can record these surface appearance features, but they do not directly measure coating thickness, coating adhesion, corrosion depth, or residual structural capacity. This study therefore focuses on the pixel-level segmentation of four visible corrosion condition states—Good, Fair, Poor, and Severe—and proposes a lightweight semantic segmentation network, termed DE-YOLO26n-sem. Built on the YOLO26n semantic segmentation baseline, the network uses a stride-2 convolution to align shallow P2/4 features with the P3/8 feature scale before injecting them into the main segmentation branch. This design helps preserve corrosion boundaries and fine surface details weakened by downsampling. An efficient channel attention module then adaptively recalibrates color and texture responses without reducing the channel dimension. Training combines weighted cross-entropy, Dice loss, and auxiliary supervision. The method is evaluated on 440 pixel-annotated images from the Corrosion Condition State Semantic Segmentation Dataset v2 using a five-fold nested procedure. In each outer iteration, one-fold is reserved exclusively for testing, while architecture screening and checkpoint selection are conducted only within the remaining development data. A6, which combines scale-matched detail injection and efficient channel attention, is selected in four of the five inner screening procedures, while A4 is selected in the remaining iteration. Across the five independent outer test folds, the nested-selected configurations, comprising A6 in four folds and A4 in one fold, achieve an mIoU of 41.52% ± 0.66%, compared with 40.76% ± 0.85% for the baseline. Severe-class F1 increases from 14.10% ± 5.60% to 18.72% ± 6.20%, while pixel accuracy changes from 82.61% ± 0.71% to 82.34% ± 0.65%. In the single-seed efficiency evaluation, the model has 1.694 million parameters and requires 1.896 GFLOPs, with a forward-pass latency of 4.07 ms for a 384 × 384 image on an RTX 4070 SUPER GPU. Ablation experiments show that a direct P2 output branch and CARAFE do not provide consistent improvements; neither is included in the final network. The resulting pixel-level maps characterize the spatial distribution of visible corrosion appearances and can support digital inspection records and the prioritization of follow-up examinations. The resulting pixel-level condition maps can support digital inspection records and help identify image regions requiring follow-up examination. The predicted classes should not be interpreted as direct measurements of coating degradation, corrosion depth, section loss, residual load-carrying capacity, or remaining service life, all of which require additional material or structural measurements.

During the long-term service and construction of bridges, various types of surface defects frequently occur in the anti-corrosion coating on the surface of prestressed metallic corrugated ducts, severely threatening the overall durability of the bridge structure. To address the challenges of uneven optical imaging, high difficulty in coating defect detection, and low manual interpretation efficiency caused by the periodic linear groove structure of the corrugated ducts, this paper proposes the BCDC-YOLO (Bridge Corrugated Duct Coating defect detection–YOLO) method, which is tailored for inspecting surface defects in corrugated duct coatings. First, the captured images of coating surface defects are augmented and processed to construct a standardized dataset suitable for training deep learning models. Second, aiming at the limitations of the original YOLO network in detecting tiny and low-contrast defects within complex corrugated groove backgrounds, four improvement strategies are introduced to optimize the network model from both microscopic and macroscopic dimensions: deformable convolution, the contrast-enhanced adaptive SiLU (CE-ASiLU) activation function, the efficient channel attention (ECA) module, and an improved dual-index Gaussian Wasserstein distance loss function. Finally, the optimized network model is utilized to train and predict the surface coating defect dataset, and the identification outcomes are thoroughly compared and discussed with existing mainstream detection methods. The results demonstrate that the proposed method achieves a mean average precision (mAP) of 95.42 across all coating damages, with an inference speed of 66.8 FPS, which far surpasses the industrial real-time detection threshold. Notably, for hidden and extremely elusive tiny pitting defects, the single-class average precision (AP) yields an improvement of 12.46 compared with the baseline network. Ablation experiments confirm the effectiveness of the four introduced optimization strategies. Compared with other mainstream detection frameworks, the proposed BCDC-YOLO achieves the optimal balance between detection precision and real-time computational efficiency, thereby providing solid methodological and technical support for the intelligent inspection of surface coating defects.