Objective Long-term, effective, and accurate measurement of the main cable force in suspension bridges is essential for the timely detection of structural anomalies and the evaluation of structural health. At present, long-term monitoring of the main cable force faces two significant challenges: low testing accuracy and high testing cost. This study presents a long-term monitoring approach for the main cable force in suspension bridges based on splay saddle displacement. Methods First, the equilibrium states of two different boundary forms, the sliding type and swing type, were discussed separately, and the calculation method for the resultant force of the anchor cable of the suspension bridge was derived. The long-span suspension bridge was regarded as a series and parallel spring system composed of various structural parts, and its structural members were simplified as springs to form an equivalent stiffness model of the series and parallel system. The cable stiffness in this model was considered a series combination of elastic tensile stiffness and linear stiffness. Based on different load forms of the cable, the longitudinal stiffness of the parabolic and catenary cables was calculated separately. The constraint stiffness of the bridge tower on the main cable was derived using the energy method and considering the influence of the second-order effect. Then, based on the balance principle at the splay saddle and the equivalent simplified stiffness model of the suspension bridge, a method for calculating the tension of the main cable using the splay saddle displacement was proposed. Taking a suspension bridge with a 660 m main span as an example, the accuracy and effectiveness of the proposed method were verified through finite element modeling analysis. A low-cost main cable force monitoring scheme was designed using a simple custom bracket and a splay saddle displacement sensor. The bridge's monitoring data for 181 days were preprocessed, and the correlation model between the splay saddle displacement and temperature was established through a polynomial fitting method. The temperature-normalized splay saddle displacement value was then obtained. The average daily displacement of the splay saddle was taken as the representative value, and both the histogram and Q‒Q plot were drawn to analyze the statistical characteristics of the data. Finally, the ARIMA model was established by selecting the first 121 cable force monitoring data points to predict the trend of data variation during the last 60 days. Results and discussions Based on the established equivalent stiffness model of the suspension bridge, the translational stiffness of the IP point of the cable saddle along its supporting surface was 1 281.7 and 1 814.9 kN/mm, respectively, which were 1.71% and 1.96% different from the 1 259.7 and 1 779.4 kN/mm calculated by ANSYS. This finding indicated that the calculation results of the proposed method were accurate and reliable. The least squares method was utilized to fit the correlation model of the saddle displacement and temperature, and the results showed an obvious linear correlation between the two. The analysis of single-day monitoring data revealed that the variation in the main cable force on that day was less than 540 kN, and the finite element calculation results were consistent with those obtained by the proposed method, with a maximum difference of 8.8 kN, accounting for only 1.66% of the daily variation in the main cable force. This finding confirmed that the method achieved high accuracy. The histogram of splay saddle displacement data presented a bell-shaped curve with a high center and low sides, approximately symmetrical, and the trend and magnitude of the probability density function curve fitted based on the normal distribution were consistent. In the Q‒Q plot, most of the data points were located within the 95% confidence interval, densely distributed in the middle, and symmetrically aligned near the reference line on both sides. It was concluded that the splay saddle displacement data followed a normal distribution. The analysis of 181 days of monitoring data showed that the main cable force changed randomly, with no evident pattern or long-term trend. The maximum, minimum, and average values of the main cable force were 117 559, 114 712, and 115 919 kN, respectively. The average and maximum stresses of the main cable reached only 67.5% and 68.5% of the standard limits, indicating that the structure is in good condition and has sufficient bearing capacity. The variation range of the main cable force was 2 847 kN, accounting for only 2.5% of the mean cable force, indicating that most of the main cable force originated from dead load, while the variation caused by live load was minimal. Compared to the measured data, the established ARIMA model provided a smoother predicted value curve for the last 60 days of the main cable force. The maximum prediction deviation was 358 kN, representing only 0.3% of the mean main cable force, indicating that the established ARIMA model achieved high prediction accuracy. Conclusions The proposed method for calculating the main cable force of a suspension bridge based on splay saddle displacement is highly accurate and reliable. There is a clear linear correlation between splay saddle displacement and temperature. The data of splay saddle displacement after temperature normalization follow a normal distribution, and the fluctuation of the main cable force remains minimal during the monitoring period. Most of the main cable force is attributed to the dead load. The main cable structure of the bridge is in good condition and possesses sufficient bearing capacity. The established ARIMA model can accurately predict the magnitude and trend of changes in the main cable force, providing a reliable basis for assessing structural anomalies and performance degradation.
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