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Carbon Fiber Reinforced Polymer (CFRP) can be used to reinforce steel structures depending on its high strength and lightweight resistance. To analyze and evaluate the load-carrying capacity of CFRP-reinforced steel structures. This study uses the Finite Element Analysis (FEA) and the experimental tests combined to investigate the influence that the reinforcement patterns and the relevant parameters have on the load-carrying capacity. We made specimens with different reinforcement patterns. Take the steel beam specimen with full reinforcement as an example. Compared with the load-carrying capacity of the steel beam reinforced by two-layer CFRP cloth, that respectively increases by 5.16% and 11.1% when the number of the CFRP cloth increases to four and six, respectively. Based on a specimen set consisting of CFRP-reinforced steel structures under different reinforcement patterns, the random forest algorithm is used to develop an evaluation model for the load carrying. The performance test results show that the MAE (Mean Absolute Error) of the evaluation model can reach 0.12 and the RMSE (Root Mean Square Error) is 0.25, presenting a good prediction accuracy, which lays a solid foundation for the research on the CFRP-based reinforcement technology and process.

Due to their ability to prevent or slow the spread of fires, fire-retardant coatings are utilized as the main means of fire protection for steel structures, combining easy application and high economic efficiency. This study investigates the effects of the particle size and dosage of expanded vermiculite (EV) on the fire resistance and application performance of coatings. Ammonium polyphosphate, melamine, and pentaerythritol were used as intumescent fire-retardant systems, along with waterborne hydroxyl-modified acrylic resins as the film-forming substances. The properties of fire resistance coatings were tested via scanning electron microscope (SEM), X-ray diffractometry (XRD), thermogravimetric analysis (TGA), limiting oxygen index (LOI), and cone calorimetry. The excellent fire performance of the coatings with 3 wt.% 300-mesh EV was proven, exhibiting a relative expansion of 30.43 times. Moreover, the surface structure of the charcoal layer was dense. The total smoke production (TSP) and smoke concentration (TSR) were only 0.18 m 2 and 0.25 m 2 /m 2 .

Steel structures are susceptible to corrosion due to their exposure to the environment. Currently used non-destructive techniques require inspector involvement. Inaccessibility of the defective part may lead to unnoticed corrosion, allowing the corrosion to propagate and cause catastrophic structural failure over time. Autonomous corrosion detection is essential for mitigating these problems. This study investigated the effect of the type of encoder-decoder neural network and the training strategy that works the best to automate the segmentation of corroded pixels in visual images. Models using pre-trained DesnseNet121 and EfficientNetB7 backbones yielded 96.78% and 98.5% average pixel-level accuracy, respectively. Deeper EffiecientNetB7 performed the worst, with only 33% true-positive values, which was 58% less than ResNet34 and the original UNet. ResNet 34 successfully classified the corroded pixels, with 2.98% false positives, whereas the original UNet predicted 8.24% of the non-corroded pixels as corroded when tested on a specific set of images exclusive to the investigated training dataset. Deep networks were found to be better for transfer learning than full training, and a smaller dataset could be one of the reasons for performance degradation. Both fully trained conventional UNet and ResNet34 models were tested on some external images of different steel structures with different colors and types of corrosion, with the ResNet 34 backbone outperforming conventional UNet.

This paper presents the results of a test of epoxy anticorrosion coating repair. Two strategies of coating repair were studied, respectively: partial repair and integral repair. The gloss loss rate, color difference and rust area rate of the repair coating were measured. The anticorrosion performance of repair coatings under different repair methods was compared. Based on the results of the coating repair test, the repair criteria for coatings were defined, and the repair strategy for epoxy anticorrosive coatings on steel structure was proposed. The results show that Marathon 500 super abrasion resistant epoxy paint (M500) and Jotamastic 90 GF low surface treatment epoxy abrasion resistant glass flake paint (J90GF) are suitable for partial repair of epoxy anticorrosive coatings on steel structures, and that Jotamastic 90 low surface treatment epoxy abrasion resistant coating (J90) and Marathon 500 super abrasion resistant epoxy paint (M500) are suitable for integral repair of epoxy anticorrosive coatings on steel structures.

Many studies have been carried out for understanding the mechanism of failure of RC structural systems under varying temperature circumstances. Similarly, several methods have been developed for improving the fire resistance of concrete buildings, with respect to material selection and detailing aspects. However, only some of the researchers have concentrated on composite behavior of interaction between the structural elements and functional elements, like interaction between masonry infill walls and RC frame elements in the emergence of fire. Several numerical models have been developed for analysis of infilled frames. For simulating the action of fire on full-scale reinforced concrete buildings, three factors, namely, presence of loading, place of fire, its intensity, and duration, must be considered since the material behavior depends on the stress level, intensity and duration of fire and the sensitiveness of structural element to the location and application of fire. To consider the combined effect of load and high temperature, finite element analysis is used. For simulating the intensity and time factor for temperature rise, transient state studies must be done., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The continuous and intensive development of measurement technologies for reality modelling with appropriate data processing algorithms is currently being observed. The most popular methods include remote sensing techniques based on reflected-light digital cameras, and on active methods in which the device emits a beam. This research paper presents the process of data integration from terrestrial laser scanning (TLS) and image data from an unmanned aerial vehicle (UAV) that was aimed at the spatial mapping of a complicated steel structure, and a new automatic structure extraction method. We proposed an innovative method to minimize the data size and automatically extract a set of points (in the form of structural elements) that is vital from the perspective of engineering and comparative analyses. The outcome of the research was a complete technology for the acquisition of precise information with regard to complex and high steel structures. The developed technology includes such elements as a data integration method, a redundant data elimination method, integrated photogrammetric data filtration and a new adaptive method of structure edge extraction. In order to extract significant geometric structures, a new automatic and adaptive algorithm for edge extraction from a random point cloud was developed and presented herein. The proposed algorithm was tested using real measurement data. The developed algorithm is able to realistically reduce the amount of redundant data and correctly extract stable edges representing the geometric structures of a studied object without losing important data and information. The new algorithm automatically self-adapts to the received data. It does not require any pre-setting or initial parameters. The detection threshold is also adaptively selected based on the acquired data.

In recent years, many seismic retrofitting methods have been performed to improve the structural performance and prevent the brittle failure of structural members. In the case of steel structures, slender seismic braces have been widely used for buildings, towers, and bridges. The brace connections should resist the full plastic axial tension load to ensure adequate plastic deformation performance for vibration energy absorption. However, certain connections do not satisfy these requirements. Recently, carbon fiber reinforced plastic (CFRP) has been used extensively to strengthen existing structures because of its high-strength, high elastic modulus, and light-weight characteristics. In this paper, we investigate the applicability of CFRP strengthening for brace connections and gusset plates with stepped surfaces using the vacuum-assisted resin transfer molding technique as a pilot demonstration. Stepped surfaces can be eliminated by using alternative CFRP layers to straighten the structural CFRP layers in order to effectively transfer the axial stress. Eventually, it is shown that CFRP strengthening can improve the connection strength and plastic deformation with 3% elongation, even if the CFRP is molded on the stepped surface.

In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate bolt-loosening angles of the identified bolts. The following approaches are performed to achieve the goals. Firstly, a RCNN-based autonomous bolt detection scheme is designed to identify corroded and clean bolts in a captured image. Secondly, a HLT-based image processing algorithm is designed to estimate rotational angles (i.e., bolt-loosening) of cropped bolts. Finally, the accuracy of the proposed framework is experimentally evaluated under various capture distances, perspective distortions, and light intensities. The lab-scale monitoring results indicate that the suggested method accurately acquires rusted bolts for images captured under perspective distortion angles less than 15° and light intensities larger than 63 lux.

To detect the stress of steel structures and members using the existing magnetism, a magnetic stress sensing system integrating a magnetic flux induction coil, a magnetic flux measurement device, a loaded device, and data acquisition software was developed. The magnetic coupling test research was carried out for different grades of structural building and bridge steel specimens to establish the magnetic stress flux mathematical model, and the fitting equation of the magnetic flux changes with the positions of different sections of specimens was analyzed. Furthermore, a practical formula for stress detection was obtained through the experiments. Meanwhile, on these bases, the typical steel truss structure model of a Bailey beam was designed and manufactured under different working conditions, nondestructive online stress testing was carried out, and the stress of the model structure and its members was measured by strain and magnetic flux tests to obtain the curves of the test results for the stress-strain and magnetic stress flux, respectively. The results of these two methods are in good agreement with each other. The stress of the steel truss model structure was analyzed and calculated using the finite element method. The results agreed well with the experimental results from the magnetic stress sensing system-the maximum error was about 5%, which meets the requirements of engineering applications.