Under actual operating conditions, structures with bolted connections can develop localized bolt loosening due to external excitations, which may result in structural disintegration or other hazards. To tackle this issue, the current study presents the theoretical model for the analysis of natural characteristics of a fiber‐reinforced composite thin‐walled cone–cylinder joint shell (FTCCJS) under conditions of partial bolt loosening. Firstly, the classical laminated plate theory and Love’s thin-shell assumption are utilized to establish a theoretical model of the FTCCJS under partial bolt loosening, and the virtual artificial spring technique (primary and secondary springs) is introduced to equivalently represent arbitrary bolt connection states. Then, an orthogonal polynomial method is employed to construct the displacement field function, and in combination with the Rayleigh–Ritz method, the natural characteristics of the FTCCJS with partially loosened bolts are solved. Subsequently, using a TC300/epoxy resin-based FTCCJS as the test specimen, a modal experiment with single-point hammer excitation and multi-point response measurement is carried out. Through comparison of experimental results and theoretical calculations, it is found that the error in the first four natural frequencies of the FTCCJS with partial bolt loosening ranges from 0.3% to 5.5%. Furthermore, the mode shapes derived from both methodologies show a high degree of congruence, confirming the validity of the theoretical model. Finally, by varying the number, degree, and pattern of bolt loosening, the impact on the natural frequencies of the FTCCJS is evaluated. This research offers valuable reference data for forecasting the natural characteristics of bolt-connected shell structures.

Fatigue performance of bolts in end-plate connections under axial tension and bending

Fatigue performance of Grade 10.9 M20 high-strength bolts in L-shaped component connections

Shear behavior of demountable high-strength bolt connectors for prefabricated steel-UHPC composite beams

Loosening of bolt connections in industrial pipeline systems poses significant risks to structural integrity and operational safety. However, conventional detection methods often suffer from low efficiency and poor robustness in complex environments. To address these challenges, this study proposes a lightweight and real-time bolt loosening detection framework based on active guided waves and multi-channel piezoelectric sensing, enhanced by advanced deep learning techniques. Specifically, an eight-channel piezoelectric sensor array is used to capture guided wave responses, which are transformed into two-dimensional (2D) representations via multi-scale feature fusion and local enhancement to facilitate deep learning. A total of 34 complex loosening scenarios—including single, adjacent, diagonal, and multi-bolt combinations—are experimentally simulated under diverse noise conditions to emulate real-world disturbances. An improved ResNet18 architecture is developed by integrating a multi-head attention mechanism for capturing long-range dependencies, along with a Squeeze-and-Excitation (SE) module for adaptive channel recalibration. Experimental results show that the proposed model achieves 99% detection accuracy under noisy conditions, with inference latency ranging from 7.2 to 10.1 ms and a throughput of 114–163 FPS (frames per second), fulfilling real-time requirements. Ablation studies confirm the effectiveness of the attention and SE components. Compared with deeper models such as ResNet50 and VGG16, the proposed method significantly reduces parameter count (13.02M) while maintaining competitive performance, enabling efficient edge deployment. Furthermore, few-shot learning experiments demonstrate that over 90% accuracy can be achieved with only five training samples in previously unseen working conditions. This research provides a robust, efficient, and scalable solution for intelligent structural health monitoring of pipeline bolt assemblies and offers valuable insights for fault diagnosis under complex industrial noise environments.

Research on Magneto-Acoustic Combined Stress Detection of Flange Connection Bolts Under Eccentric Loading Conditions

Existing literature and practical engineering practice have comprehensively examined the behaviour of concrete-filled steel tubular (CFST) columns, CFST beams, and their associated connection systems involving either steel or reinforced concrete (RC) beams. Despite these advancements, limited research has focused on the direct beam–column interaction in fully CFST-to-CFST connection configurations. The absence of established design specifications and systematic experimental evidence has hindered the reliable adoption of such connections in structural applications. This feasibility study addresses this knowledge gap by conducting an integrated theoretical and experimental investigation into the structural performance of moment-resisting connections between CFST columns—locally strengthened with internal stiffening plates and configured with external bolted flange connections—and CFST beams of matching geometry. To provide a meaningful benchmark, a comparable hollow steel column–steel beam connection with identical cross-sectional dimensions and bolt arrangements was also evaluated. The experimental setup involved cyclic loading tests designed to capture load–rotation behaviour, quantify flexural stiffness, and identify critical limit states governing connection performance. Detailed measurements of moment–displacement response, local deformation patterns, and strain distribution were collected to assess connection rigidity, load-transfer mechanisms, and potential vulnerability to local buckling. The resulting data allowed for direct comparison between the proposed CFST-to-CFST connection configuration and the hollow steel reference specimen, enabling a clearer understanding of the composite action and confinement effects provided by the infilled concrete. The findings contribute foundational evidence for the feasibility of those moment connections and offer preliminary insights to support future analytical modelling, design recommendations, and full-scale implementation.

Abstract This publication presents the first part of a comprehensive study on a novel methodology for real‐time monitoring of preload in high‐strength bolted connections, applied to a standardised beam‐to‐column frame corner. The approach combines electromechanical impedance (EMI) measurements with convolutional neural networks (CNNs) to enable a scalable, robust, and industrially applicable solution for structural health monitoring (SHM). This first part focuses on demonstrating the applicability of the EMI‐CNN concept, explaining the underlying physical principles, and developing an automated evaluation environment. Two experimental series were conducted on a standardised frame corner specimen with M16 HV bolts. EMI spectra were acquired using surface‐mounted piezoelectric transducers and processed with trained CNN models to quantify the preload. In the conducted experiments, the model achieved a mean absolute error (MAE) of 1.07 % with respect to the applied preload. An interactive software tool was developed to support practical application, including visualisation of the bolted connection, model import, and automated real‐time evaluation of new EMI measurements. The preload level is displayed as a percentage, while an integrated traffic light system enables intuitive real‐time condition assessment. The results confirm the feasibility and reliability of the EMI‐CNN framework for preload determination and provide a solid basis for scalable industrial implementation using pre‐calibrated, sensor‐integrated bolts.

The service performance of high-end equipment with bolted connections depends on the connection quality of the bolts, which is greatly affected by the tightening process. During the assembly process, improper tightening techniques can lead to structural failure and bolt breakage, seriously affecting the lifespan and safety of equipment. Furthermore, bolt connection quality is determined by bolt preload and the thread contact state. Currently, measuring bolt preload during the tightening process is mostly achieved by installing sensors, which alters the original structural state. Additionally, due to the need to install sensors for each bolt, this method cannot be widely used in engineering. Furthermore, during the tightening process, when the stress at the thread root is about to exceed the tensile strength, excessive torque can cause the bolt to break. However, there are few methods with which to obtain changes in stress at the thread root and effectively make relevant predictions. To address the aforementioned challenges, this paper proposes a bolt tightening digital twin method based on deep learning. Initially, the bolt tightening rotation angle is obtained through a target detection algorithm. Then, a mapping model between the angle, preload and thread contact stress is established and a bolt tightening digital twin model is constructed. Finally, through experimental verification, the experimental results reveal that the error decreases with the increase of axial force and the error between the experiment and simulation is within 10%. This method does not require additional sensors and it only needs to obtain the bolt rotation angle to obtain changes in axial force and thread contact stress. Additionally, when the stress at the thread root is about to exceed the tensile strength, an early warning is given to prevent bolt breakage. This method provides technical support for high-quality bolt tightening in the assembly of high-end equipment.

Experimental and theoretical study on mechanical behavior of high strength bolt connections for prefabricated steel-CLBT composite floors

Abstract Corrosion failure of transformer terminal connection components poses a severe threat to the service safety of power equipment in coastal regions. This study employs neutral salt spray corrosion testing to simulate the high-temperature summer conditions along Guangzhou’s coastline, investigating the corrosion behaviour of H59 brass transformer wiring clamp. Results indicate that corrosion intensifies under the synergistic effects of galvanic and crevice corrosion when H59 brass is connected to galvanized bolts, with the hot-dip galvanized bolt connection group exhibiting the most severe corrosion. The primary corrosion products of H59 brass in salt spray environments are Cu 2 O and ZnO. However, following connection with galvanised bolts, the Cu 2 O phase disappears due to the participation of coating elements in the reaction, with additional oxides such as Sn 2 O appearing. Compared to the loose ZnO, the ability of H59 brass to form dense Cu 2 O may be key to its resistance to salt spray corrosion.

Flange connections are widely used in various fields such as civil engineering, aerospace, and mechanical manufacturing due to their convenient installation and reliability. However, the bolts in the flange connections may become loose, which could affect the safety of the connections. Nowadays, the vision-based detection techniques are widely used because of its ability to provide quantitative loosening angles, high efficiency, and low cost. However, the vision-based detection techniques are susceptible to the influence of perspective angle and it is hard to provide the historical information of bolt automatically. Therefore, the quick response code is introduce to record the historical information of bolt and a novel bolt image correction method is proposed based on quick response code. Furthermore, the anti-loosening bolt looseness diagnosis method is established. Firstly, the quick response code is designed with three finder patterns and pasted on the end face of screw. And then, the four intersections of quick response code edge lines are used to correct the image based on the homography-based perspective rectification method. Additionally, three finder patterns are used to rotate the image to the unified reference state to reduce the effects of camera position deviation and simultaneous rotation of screws and nuts. Finally, the anti-loosening bolt looseness diagnosis method is established by using the change in rotation angles of nut under initial status and loose status. A prototype flange node was used for experimental verification. The results show that the proposed method can effectively correct the perspective distortion of bolt image, reduce the effects of camera position deviation and simultaneous rotation of screws and nuts, and detect the loosening angle of anti-loosening bolts.

This study investigates the structural response of engine outer casings subjected to external pressure, with particular emphasis on the buckling resistance and sealing integrity of split-structure configurations. The analysis demonstrates that split mounting edges significantly affect both the critical buckling pressure and the associated buckling modes, with more pronounced effects observed when failure initiates near the edges. When buckling occurs away from the mounting edges, the ultimate pressure of the split-structure casing exceeds that of a continuous casing due to the axial reinforcement provided by the edges. Nonlinear buckling analyses further reveal that the ultimate pressure increases with mounting edge thickness, although the improvement is limited. The inclusion of bolt connections and contact effects produces only minor variations in the predicted buckling capacity. However, under external pressure loading, the separation and the subsequent clearance growth at the aft end of the mounting edges compromise sealing integrity and may result in leakage. These findings contribute to a deeper understanding of the structural behavior of split-structure casings and provide guidance for their design and reliability assessment.

Abstract The computation of bolts in pipe flange connections has received significant attention in engineering practice. However, few studies have systematically been done to compute member stiffness in the design of tie bolts under nozzle loads. This paper focuses on the critical problem of foot bolt group connections in pressure vessels subjected to nozzle loads, particularly when these loads exceed standard requirements. A method based on the translation of spatial force systems is proposed to determine the equivalent loads on bolt groups. Four primary failure modes including faying translation, faying rotation, angle separation, and parallel separation are analyzed, and corresponding calculation procedures are established. Two distinct methods are proposed to further evaluate the bolt group's strength, and calculation examples and contrast analysis are performed to verify their applicability at different development stages.

Comparative analysis of dynamic response behavior between welded stud and demountable bolt connectors in steel-UHPC composites

Due to structural characteristics and connection dimensions, the dynamic characteristics of dual metal rubber clamps (DMRCs) show significant differences in bolt connection direction and opening direction. Accurately identifying the dynamic parameters of DMRC in different directions is of great significance for analyzing the dynamic characteristics and vibration control of aero-engine piping systems. This paper takes a DMRC-double straight pipe structure as the research object and establishes a dynamic model of this structure based on the finite element method as the mechanical parameter identification model of DMRCs. A refined simulation mechanism is adopted in the model to reflect the dynamic characteristics of the DMRC. The DMRC is simplified into four concentrated mass blocks and four spring-damping groups to simulate its mass, stiffness, and damping effects. Each spring-damping group consists of a linear spring, a rotational spring, and a damper. The four groups of springs are further divided into two directional groups to simulate the stiffness and damping effects in the opening direction and bolt connection direction, respectively. Four concentrated mass blocks are applied to the four nodes of the pipe to simulate the mass effect of DMRCs. Based on the dynamic model of the pipeline structure mentioned above, the synchronous identification algorithms and procedures for multiple mechanical parameters of DMRCs are proposed, aiming to minimize the deviation of natural characteristic indicators (natural frequency and peak of frequency response function) obtained through testing and model simulation. This method can synchronously identify linear stiffness, rotational stiffness, and damping in different directions. Finally, the effectiveness of the identification method is verified through experiments.

ABSTRACTThis study explores the seismic design and performance of bolt‐connected prefabricated wall panel structures, addressing the challenges of achieving comparable performance to cast‐in‐place systems. Such structures are increasingly used due to their advantages in construction speed, sustainability, and quality control, but their discrete bolt connections create unique design challenges in load transfer and joint behavior under seismic forces. The research establishes a “nonequivalent cast‐in‐place” seismic design framework, incorporating methods for calculating the initial lateral stiffness of single‐panel and multipanel systems, determining joint bearing capacity under tension and shear, and evaluating seismic force distribution using the base shear method. Numerical simulations analyze key design parameters, including the number of floors, aspect ratio, and transverse wall spacing. Numerical models reveal that the maximum applicable height and number of floors for seismic zones rated 7° and 8° are six floors (21 m) and five floors (18 m), respectively, with corresponding limits for aspect ratios and wall spacings. This study contributes a tailored seismic design methodology for bolt‐connected prefabricated structures, promoting resilient, cost‐effective solutions for sustainable construction in seismic‐prone regions. By bridging the gap between prefabricated and cast‐in‐place systems, the findings offer actionable guidance for engineers and designers seeking to optimize structural performance.

This study presents a novel approach for autonomous bolt-loosening monitoring by integrating computer-aided design (CAD)-assisted PointNet deep learning and three-dimensional (3D) point cloud processing. The following approaches are implemented to achieve the objective. First, a procedure of CAD-assisted databank generation for PointNet deep learning models is proposed. The CAD-generated databank contains diverse and well-labeled 3D point clouds of a bolt connection of steel girder with variation in loosening conditions. Second, PointNet segmentation and classification models are trained based on CAD-generated databank. The PointNet segmentation model is used to identify bolts from 3D point cloud, and PointNet classification models are used to identify bolt head angles and loosening lengths. Third, the CAD-assisted PointNet models are validated on real-world 3D point clouds of a steel girder bolt connection under various bolt-loosening levels. To enhance the performance of PointNet deep learning, advanced 3D point cloud processing techniques such as representative orientation alignment, bolt isolation using K -means clustering, and best-fitting rectangle for bolt group identification are introduced. Experimental results demonstrated that the proposed method effectively identifies bolt-loosening angles with high accuracy.

Flange effects on the behavior of double-plate composite shear walls with steel fiber concrete constrained by stud and bolt connections

Bolted connection loosening poses significant risks to structural load-bearing capacity, yet reliable detection remains challenging due to geometric complexity and material discontinuities. This study develops an innovative transient impact-response method for bolt loosening detection through vibrational energy analysis. The approach utilizes controlled steel ball impacts on pipe exteriors combined with piezoelectric sensor arrays to capture flange vibration signatures. Experimental results demonstrate a strong correlation between received signal energy attenuation and bolt torque reduction from 50 N·m to complete loosening. The method provides four significant benefits: (1) non-invasive operation without disassembly requirements, (2) highly cost-effective implementation using minimal equipment, (3) rapid assessment capability with high detection efficiency, and (4) simplified operation requiring minimal operator expertise. Validation tests confirm the technique’s superior performance for pipeline integrity monitoring compared to conventional non-destructive testing methods, offering an optimal balance of accuracy and practicality for industrial applications.