Large-span Bridge Research Articles

Mechanical properties and restoring force model of Squeezing Rub-type Particle Damper

The sliding effect of isolation bearings has been proven to effectively mitigate the seismic effects on large-span bridges. However, it also results in excessive relative displacement between the main girder and piers during earthquakes. In this study, a pioneering Squeezing Rub-type Particle Damper (SRPD) is developed to address this issue. In order to verify the damping characteristics of SRPDs and investigate the working principle of squeezing friction energy dissipation, five quasi-static tests were conducted on SRPDs using two particle damping materials with different compactness levels. In order to compare the energy dissipation performance, comparative tests were conducted on Non-Squeezing-type Particle Dampers (NSPDs) and SRPDs. Utilizing regression analysis, two skeletal curve models for SRPDs were proposed, alongside a detailed restoring force model that incorporates considerations for both the pinching effect and stiffness degradation phenomena observed during the loading and unloading phases. The experimental results demonstrate that the maximum damping force of SRPDs can reach 16 times that of NSPDs, and the energy dissipation of each loading cycle can reach 21.06 times that of NSPDs. SRPDs exhibits improved energy dissipation performance when using high-density blended quartz sand. After sand replenishment repairs,damping force and energy dissipation per cycle of SRPDs were significantly enhanced. The proposed restoring force models of SRPDs aligns well with the experimental curves, underscoring its efficacy in accurately characterizing the hysteresis performance of SRPDs.

Cellular Automata-Based Experimental Study on the Evolution of Corrosion Damage in Bridge Cable Steel Wire

Cable-stayed bridges have become the preferred bridge type for large-span bridges due to their unique advantages, and the long-term performance of the cable under the extreme conditions has been facing great challenges. An accelerated corrosion test was carried out using in-service cable, and the evolution model of the etch pit was established based on cellular automata to study the evolution law of corrosion damage to steel wire. This study showed that with the increase in the number of dry-wet cycles in the electrified accelerated corrosion, the macro- and micromorphology of the steel wire showed more serious corrosion damage, the tensile strength decreased, the ductility index decreased, and the tensile strength of the steel wire after corrosion decreased by nearly 5%; the geometric dimension of the steel wire etch pits all met a right-skewed distribution with a broader range of etch pit depth, mainly consisting of shallow spherical etch pits and deep ellipsoidal etch pits. The length, width, and depth sizes were mainly distributed in the range of 0.005 mm to 0.015 mm, 0.005 mm to 0.02 mm, and 0 mm to 0.04 mm; at the early stage of corrosion, the etch pits were first developed along the longitudinal direction. As the corrosion process progressed, the iron matrix participated in the electrochemical reaction, leading to the rapid expansion of the etch pits’ dimensions. The stress concentration effect at the bottom of the etch pit caused the maximum stress to approach 1800 MPa, with a stress concentration coefficient of more than 3.0; when the cable anchorage system was located in the connecting sleeve and the threaded splice seam, where corrosion protection was prone to failure, the outer steel wire bore most of the corrosive effects, and the internal cable was less eroded by the corrosive medium.

Correlation analysis of long-span cable-stayed bridge strain response with environmental and operational variations based on feature selection

The structural response of large-span bridges is significantly influenced by environmental and operational conditions. This study employs a data-driven approach, which utilizes various techniques such as multiple linear regression, best subset regression, stepwise regression, Lasso regression, Ridge regression, and ElasticNet regression to analyze in detail the effects of environmental variables (temperature variables including temperature principal components, lateral temperature gradients, and vertical temperature gradients), and traffic loading on bridge strain response. Based on the analysis of 21 months of health monitoring data from a newly constructed cable-stayed bridge, Lasso regression is shown to perform best in predicting strains on the bridge deck and bottom slab. Furthermore, sensitivity analysis identifies several key influencing factors and quantifies their relative contributions to the strain. Although the 10-min average of the strain due to traffic loading has a minimal impact, the root mean square of the strain shows a significant correlation with the cumulative gross vehicle weight during busy traffic periods. The methodology proposed in this study helps to understand the strain response patterns of large-span bridges from a data perspective, and provides an effective approach to establishing a strain baseline model that eliminates environmental and operational variations.

Dynamic response of high-speed railway vehicle and welded turnout on large-span bridges based on rigid-flexible coupling system

Welded Turnout on Large-span Bridge (WTLB) is a complex multi-layer heterogeneous system and can significantly influence the service performance of High-Speed Railway (HSR). Understanding the coupling dynamic response of the vehicle and WTLB is essential. Previous research did not consider the dynamic behavior of foundations, leading to an underestimation of the vehicle-turnout-foundation coupling dynamic response, particularly when turnouts were laid on large-span bridges. This study proposes a novel modeling method that includes the foundations, to overcome the previous shortcomings by applying a rigid-flexible coupling system. In this approach, the vehicle was modeled as a rigid body sub-model in a Multi-Body Software (MBS), while WTLB was modeled as a flexible bodies sub-model using Finite Element (FE) software. The modal information from the FE model was imported into the MBS software. The two sub-models were coupled by the wheel-rail contact in the MBS environment and then the Vehicle-turnout-bridge Rigid-flexible Coupling Dynamic (VRCD) calculation model was established and it was discovered that the calculation results showed good agreement with the field test data. Through the VRCD model, the safety of the structure, the stability of the vehicle and the comfort of passengers were investigated, as well as several important infrastructure factors. The results demonstrate that this novel method provides accurate calculations and highlights the complex and significant interactions in the vehicle-turnout-bridge system.

Experimental Study and Neural Network Predictions of Early-Age Behavior of Microexpansion Concrete in Large-Diameter Steel Tube Columns

The improved mechanical performance of concrete-filled steel tube (CFST) components has led to their widespread application in megastructures. Therein, CFST hybrid arch bridge has become an optimal selection of large span bridges. Nonetheless, for massive concrete poured in CFST members with large diameter, the thermal cracking and sustained rising temperature caused by early hydration heat of core concrete are urgently required to be studied. The two large-diameter CFST columns in this work, each having a diameter of 2.1 m, had their temperature and strain fields recorded in-situ. An existing CFST arch bridge served as the model for the two CFST columns’ design. Additionally, early-age characteristics of several scaled CFST columns with varying diameters were documented. A multi-field finite element (FE) model that combines linked chemical (hydration), heat, and mechanical fields is suggested in order to properly characterize the evolutions of temperature and strain fields. The model is validated by comparing the in-situ measurements with the numerical results. Finally, to investigate the affect factors on the hydration temperature of core concrete in CFST columns, early-age hydration behaviors of CFST columns was simulated using the validated FE models input parameters as water to cement ratio (w/c), cement dosage, heat release of cement and diameter of CFST columns. Based on the numerical results under the input parameters mentioned, the LSTM neural network was constructed, and the hydration temperature variances computed by FE models were selected as the input dataset. Afterwards, the temperature variance of core concrete of CFST columns was predicted using the established LSTM network. It is discovered that the LSTM neural network that was previously constructed was able to predict the peak temperature of CFST columns as well as the hydration temperature of CFST specimens with respect to time.

Response prediction and damage assessment of CFST column after explosion via ANN

Concrete-filled steel tubes (CFSTs) have been extensively employed in pressurized structural members, such as high-rise buildings, large-span bridges or offshore structures. Accurately and reliably assessing the damage of CFST column subjected to explosion is helpful for designers to optimize the structural performance and construction cost. Currently, peak displacement, the damage indicator, is generally estimated via finite element method (FEM) and single degree of freedom (SDOF) oscillator model. However, the computational overhead of FEM is high, while the computational accuracy of SDOF model is low. In this paper, artificial neural network (ANN) is used to overcome the above drawbacks. A dataset of 270 experiments and numerical simulations is collected from existing literatures to develop the ANN model. The performance of this model is compared with the conventional methods, i.e., FEM and SDOF model. And its prediction accuracy and efficiency are better in the comparison by experimental results. The damage evaluation and parameter research are carried out to reveal insight into the factors on the CFST column under explosion, including the property of column and its cross-section properties, the axial compression condition, and the explosive. To support the explosion resistance design of a CFST column, an explicit equation and an excel calculation table are introduced based on the ANN model.

Graph neural network incorporating time-varying frequency domain features with application in spatial wind speed field prediction

Precise forecasting of wind speed is vital to guarantee safe travel on bridges. Nevertheless, current research primarily concentrates on single-point prediction. When it comes to forecasting spatial wind fields, graph neural networks prove to be powerful methods, but many of them have yet to take into account the impact of frequency domain attributes. To address this, a novel model called the graph neural network with time-varying frequency features (GTF) is proposed. Its core layer employs wavelet transforms in matrix form to extract various frequency sub-series, followed by the use of adaptive adjacency matrices to identify their spatial characteristics. Furthermore, aggregating all frequency band information and exploring spatial features further through multi-kernel convolutions. The predictive performance of different models for wind speed forecasting is assessed by utilizing spatial wind field data collected on a large-span bridge. Experimental results demonstrate that spatiotemporal model GTF outperforms the other models in terms of predictive accuracy. The quantity of wind observation points and their level of correlation exert a certain impact on the GTF model, while hyperparameters such as stacking depth and wavelet decomposition parameters have minimal effects. Finally, recommended parameters for the GTF model are provided.

An algorithm for large-span flexible bridge pose estimation and multi-keypoint vibration displacement measurement

Traditional visual vibration measurement methods face limitations in dynamic outdoor environments, making accurate multi-point vibration measurements challenging for the targeted objects. In this paper, a visual vibration measurement algorithm for large-span bridges is proposed by anthropomorphizing the bridge target and introducing key point detection at the same time. Firstly, the algorithm utilizes a convolutional neural network to extract multi-scale feature information from the image sequence of the bridge model. Secondly, to enhance target detection accuracy, a coordinate attention (CA) mechanism is incorporated, and the shape intersection over union (SIoU) loss function replaces the original loss function. Finally, the algorithm integrates the density-based spatial clustering of applications with noise (DBSCAN) and tracking algorithms to achieve precise localization of bridge targets. Vibration displacement data were extracted from a large-span suspension bridge structure in an outdoor environment and analyzed quantitatively and qualitatively. The vibration displacement curves obtained in this study show the highest level of agreement with the standard displacement signals, with a mean absolute percentage error of 0.5170% for the cable-stayed bridge model, 1.3822% for the Humen Bridge, and 3.2263% and 1.6982% for the Longjiang Bridge.

Study on the dynamic fracture mechanical properties of ultra high-performance concrete under the influence of steel fiber content

Ultra-high performance concrete (UHPC) is characterized by its ultra-high strength and durability, making it suitable for use in large-span bridges, super high-rise buildings, and other special structures. These types of structures often have high requirements for crack control, making the exploration of UHPC's fracture mechanics properties of significant importance. Therefore, this paper sets up four steel fiber contents (0 %, 0.6 %, 1.2 % and 1.8 %) and four strain rates (10−5/s, 10−4/s, 10−3/s and 10−2/s), totaling 16 working conditions. The hydraulic servo system combined with digital image correlation (DIC) technology was used to conduct fracture mechanics tests on UHPC three-point bending beams. The test results show that the unstable fracture load of UHPC increases with the increase of strain rate and steel fiber content, and increasing the steel fiber content can enhance the strain rate effect of UHPC to a certain extent. Additionally, combined with the J-criterion model, this study proposed a dynamic criterion for the unstable fracture load of UHPC considering the influence of steel fiber content. The initial fracture toughness, unstable fracture toughness, and fracture energy of UHPC are significantly more affected by steel fiber content than by strain rate. Through DIC technology, the analysis of UHPC crack propagation shows that UHPC without steel fibers experiences rapid crack expansion in the Pre-90 % to Peak stage, with less influence from the strain rate. However, UHPC with steel fibers exhibits a more uniform crack growth rate, with the crack emergence phase gradually lagging as the strain rate increases. For UHPC without steel fibers, the crack opening displacement (COD) at the unstable fracture load point surges suddenly, while the COD development in UHPC with steel fibers is slower. At lower strain rates, an increase in steel fiber content leads to an earlier sudden increase phase in UHPC's COD; at higher strain rates, the influence of steel fiber content on the sudden increase phase of UHPC's COD is more scattered. The research results of this paper provide a theoretical basis for the calculation and analysis of the dynamic response of UHPC structures under seismic loads.

Crested Porcupine Optimizer-Optimized CNN-BiLSTM-Attention Model for Predicting Main Girder Temperature in Bridges

Stage-built long-span bridges deform with temperature, affecting alignment to design needs. In this paper, a model for predicting temperature time series is proposed, which can predict temperatures in engineering practice and utilize the predicted results to adjust the elevation of stage construction. The model employs convolutional neural networks (CNNs) for initial feature extraction, followed by bidirectional long short-term memory (BiLSTM) layers to capture temporal dependencies. An attention mechanism is applied to the LSTM output, enhancing the model’s ability to focus on the most relevant parts of the sequence. The Crested Porcupine Optimizer (CPO) is used to fine-tune parameters like the number of LSTM units, dropout rate, and learning rate. The experiments on the measured temperature data of an under-construction cable-stayed bridge are conducted to validate our model. The results indicate that our model outperforms the other five models in comparison, with all the R2 values exceeding 0.97. The average of the mean absolute error (MAE) on the 30 measure points is 0.19095, and the average of the root mean square error (RMSE) is 0.28283. Furthermore, the model’s low sensitivity to data makes it adaptable and effective for predicting temperatures and adjusting the elevation in large-span bridge construction.

Evolution of microstructure and mechanical properties along the thickness direction of 500 MPa HSLA steel heavy plates

High strength heavy plates with large thickness are demanded for the construction of large-span bridge projects. However, there is a lack of understanding on the microstructure evolution and mechanical properties through the entire thickness of heavy plates with yield strength over 500 MPa, especially manufactured by the thermo-mechanical control processing (TMCP) and tempering (T) process. In this research, the microstructure and mechanical property evolution through the thickness was investigated for the 85 mm thick, 500 MPa grade heavy steel plate manufactured by TMCP + T. The results show that the microstructure throughout the thickness predominantly consists of granular bainite (GB) and polygonal ferrite (PF). The yield strength and tensile strength at 1/8t, 1/4t, 3/8t and 1/2t (t, the thickness) are 659.4 MPa, 520.6 MPa, 500.3 MPa and 494.0Mpa, and 739.2 MPa, 642.9 MPa, 627.8 MPa and 625.2 MPa, respectively. Yield and tensile strength both significantly decline from 1/8t to 1/2t. The strengthening mechanisms at different thickness were analyzed, and grain refinement has the most significant contribution. In addition, the mechanical properties of ferrite/bainite heterostructure were analyzed by Cyclic load-unload-reload (LUR) tests. The hetero-deformation induced (HDI) stress increases from 1/8t to 1/2t. At 1/8t, HDI hardening is higher but occurs over a narrower strain range compared to that at 1/2t. The impact energy at −40 °C for 1/8t, 1/4t, 3/8t and 1/2t are 182.7 J, 213.0 J, 242.2 J and 253.7 J, respectively. It was discerned that PF undergoes plastic deformation and absorbs a large amount of energy, constituting the primary rationale for the enhanced impact toughness at 1/2t compared to 1/8t. Through this research, not only the microstructure and mechanical property evolution through the thickness was revealed, but also the ferrite/bainite microstructure was found a promising heterostructure to obtain high strength, high toughness and low yield ratio for heavy plates.

Seismic Study of An Isolated Cable-Stayed Bridge under Near-Fault Ground Motions

During strong earthquakes, pounding may occur on large-span bridges and their approach bridges. The effect and mitigation measures of such pounding have rarely been explored in previous studies. This paper primarily uses finite element models to investigate the pounding effects at the expansion joints between the main cable-stayed bridge and its approach bridge. Friction pendulum bearings (FPBs) and fluid viscous dampers (FVDs) are used to alleviate poundings. Furthermore, a detailed analysis is conducted on how the pounding effect of the isolated main bridge with FPBs and FVDs is affected by the wave passage effect, ground motion type, and soil type. This study reveals that FPBs and FVDs can effectively reduce pounding effects and the associated risks. Even with the installation of FPBs and FVDs, lower seismic wave velocities and near-fault seismic motions with pulse effects can significantly increase the pounding effects between the cable-stayed bridge and its approach bridge.

MEEMD-Based Ground Vibration Component Extraction and Response Analysis of Long Period Ground Vibration on High-Pier Large-Span Bridges

MEEMD-Based Ground Vibration Component Extraction and Response Analysis of Long Period Ground Vibration on High-Pier Large-Span Bridges

Shaking Tables Test on Seismic Responses of a Long-Span Rigid-Framed Bridge Considering Traveling Wave Effect and Soil–Structure Interaction

The traveling wave effect and soil–structure interaction have significant influence on the seismic response of large-span bridges with complex site conditions. In this paper, a 1/10 scaled-down large-span rigid-framed bridge model was designed and fabricated, and a shaking tables test considering the traveling wave effect and soil–structure interaction was carried out on a large-scale continuous rigid bridge model by a real-time substructure hybrid test technique. Influences of the traveling wave effect and soil–structure interaction on the seismic responses of the rigid-framed bridge specimen were systematically analyzed with experimental data. The test results showed that when the apparent wave speed was small, the traveling wave effect increased the seismic responses of the rigid-framed bridge. With the increase in apparent wave speed, the structural response under traveling wave excitation and uniform excitation was basically the same. The SSI effect lead to a great change in the seismic input peaks and spectral characters at the bottom of the pier, and increased the seismic responses of the rigid-framed bridge. When both traveling wave and the SSI effect were considered, there was a phase difference in the seismic excitation. The dynamic responses of a continuous rigid-framed bridge could not be simply obtained by superposition of the separate traveling wave effect or SSI effect. Meanwhile, the real-time substructure test method in this paper solved the problems that the traditional soil box experiment cannot be applied to the test of a large-scale model, the soil and bridge structure find it difficult to meet the unified similarity ratio, and the boundary conditions are difficult to simulate accurately.

Real-Time Deformation Measurement of Long-Span Bridge using Multi-Inertial Camera System

Abstract. Monitoring the deformation of long-span bridges is essential for assessing structural health and safety. The existing single-camera deformation measurement method is unable to meet the high-precision measurement requirement for the deformation of large-span bridges. This paper proposes a real-time deformation measurement method for the long-span bridge using multi-inertial camera system. The method is based on visual measurement which the target deformation is measured by multiple cameras. Meanwhile, inertial measurement is used to estimate the relative pose of the camera to compensate for camera motion errors. It is applied to the health monitoring system of a long-span bridge. The experimental results show that the method proposed in this paper is highly consistent with the hydrostatic leveling measurement, with a RMSE of 4.83mm. The method can accurately and real-time measure the deformation of large-span bridges with a promising engineering value.

Stability analysis of long-span bridge under static wind load

With the maturity of various technologies and the vigorous development of advanced materials, the design and construction of suspension bridges require larger spans and lighter structures, but at the same time, the problems arising from the dynamic and static responses of the system under wind loads have become more and more prominent. The phenomenon of wind-induced static response occurring before the emotional response has been found in wind tunnel tests at home and abroad. Static instability is also a vital issue in the wind-resistant design of large-span suspension bridges, so studying the static behavior and instability law of large-span suspension bridges under strong winds is of high practical value for the construction of large-span bridges in the future. In this paper, relevant research results of previous researchers based on static wind stability of suspension bridges are sorted out and summarized. Their corresponding fundamental theories are introduced in detail. In addition, the related analysis program is prepared by using the APDL language of ANSYS.

Study on Fatigue Life of PC Composite Box Girder Bridge with Corrugated Steel Webs under the Combined Action of Temperature and Static Wind Loads

The large-span bridge is highly sensitive to temperature and wind loads. Therefore, it is essential to study the bridge’s fatigue life under the combined effects of temperature and static wind loads. This study focuses on the main bridge of Qiao Jia-fan 2# on the Yinkun Expressway (G85), with a span of 250 m and a configuration of a PC composite box girder bridge with corrugated steel webs. Firstly, on-site temperature and wind direction measurements with wind speed were conducted at the bridge site. Origin 2022 software is used to make mathematical statistics on the data, the representative values of atmospheric temperature difference between day and night and the basic wind speeds are calculated. Secondly, based on the basic wind speed in the most unfavorable wind direction, the static three-component force coefficients of bridge at different angles of attack are calculated by FLUENT 2022 R1 software. By comparison, the most unfavorable wind angle of attack, wind direction and wind load value of Qiao Jia-fan 2# Bridge are obtained. Finally, the finite element software MIDAS/FEA NX 2022 is used to analyze the fatigue life of the main bridge of the Qiao Jia-fan 2# Bridge. The analysis results show that the representative value of the temperature difference between day and night in the area where PC composite box girder bridge with corrugated steel webs is located is 22 °C, the most unfavorable wind direction is NNE wind direction, and the most unfavorable wind attack angle is 3° wind attack angle. It is found that the maximum stress of concrete and corrugated steel webs appears near the 0# block, and the life of corrugated steel webs is far greater than that of concrete.

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds.

With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher R2 values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading.

Static and Dynamic Performance Analysis of Cable-Stayed Bridges with Cables Damaged Fire

Cable-stayed bridges have been widely used in large-span bridge engineering because of their large span capacity and novel structure. The frequent traffic of vehicles transporting flammable and explosive materials has increased the incidence of bridge fires. After being burned, the cable-stayed bridge will suffer from varying degrees of damage, which affects its performance. Therefore, mechanical analysis and evaluation of the fire-damaged cable-stayed bridge are necessary. Due to the development of technology, the structural analysis of cable-stayed bridges has gradually shifted from experimental methods to numerical simulation or artificial intelligence methods, and from local performance research to holistic research. In this paper, a fire accident in the Sifangtai Bridge in Harbin, China, is taken as a case study. Finite element software and damage theory calculation methods were used, and the static and dynamic performances of the bridge under the condition of cable fire damage were analyzed. The results show that the variation of cable force during the movement of vehicle load along the bridge is relatively small, within the range of 7% to 12%. The fusing of the upper cables of the bridge tower has the greatest impact on the deflection of the beam, while the fusing of lower cables has the same impact on the deflection of the entire bridge as the undamaged state. Near the fused cables, cable forces change significantly, increasing by over 20%. As the degree of damage increases, the increase in deflection of the beam becomes more pronounced. The impact of different degrees of cable damage on the dynamic performance of cable-stayed bridges is reflected in quantitative changes. As the degree of cable damage and the amount of fusing increase, the change in structural frequency becomes more pronounced. This paper not only provides technical support and a theoretical basis for the performance analysis of cable-stayed bridges damaged by fire, but it also improves the research content of combining static and dynamic performance, which provides important reference values for similar research in the future.

Computer vision-based real-time continuous monitoring of the pose for large-span bridge cable lifting structures

Given the highly intricate terrain and environmental conditions in the mountainous region, the existing measurement methods fail to ensure high precision and real-time long-distance measurements. The present study proposes a methodology for real-time and continuous monitoring of the bridge cable lifting structure pose over long distances. The main contributions include: (1) Designing a framework for a monocular vision-based 6 degrees of freedom (6-DOF) pose monitoring system that enables real-time measurement of bridge cable lifting structure displacements and angles during the construction of large-span bridges. (2) Developing an improved approach to address potential mislocalization of coordinate origin in the sequence ordering method used in traditional corner detection when the target proportion relative to the captured image area is small. The effectiveness of the proposed method is verified through the theoretical simulation tests, laboratory experiments, and monitoring of the actual use of the cable lifting structure during the construction of the XiNing River Extra large bridge.