Structural System Reliability Research Articles

An efficient analysis method for system reliability of semi-rigid base asphalt pavement structure based on direct probability integral method

The structural design parameters of asphalt pavement inherently possess randomness. Reliability theory aids in considering the stochastic nature of these parameters in asphalt pavement design. However, as the number of considered random factors increases, the complexity of reliability calculation models and the consumption of computational resources also significantly escalate. To analyze the structural reliability of semi-rigid base asphalt pavements more efficiently and accurately, a method combining Weighted Kernel Density Estimation (Weighted-KDE) with Direct Probability Integration Method (DPIM) using sampling strategies was employed. Furthermore, integrating the equivalent extreme value principle, an asphalt pavement structural system reliability analysis method was proposed, considering multiple failure mode correlations. The numerical solution steps of this method are also outlined. Based on the typical semi-rigid asphalt pavement structure in China, this study utilized the elastic layer system method (the program Apbi) and a computational program developed in MATLAB to compare the efficiency and accuracy of calculating reliability between DPIM and the Monte Carlo Simulations (MCS). The computational outcomes indicate that, under the premise of selecting 18 random parameters, DPIM can achieve the required accuracy in assessing the reliability of asphalt pavement structure and system reliability. Analyzing how the number of samples affects the computational results reveals that using weighted-KDE and DPIM only needs a few hundred sample points to limit the relative error within 4 %, compared to the results obtained from 105 samples in MCS. The research findings demonstrate that DPIM incorporating the principle of equivalent extreme value events can serve as an efficient analytical approach for assessing the system reliability of asphalt pavement structures characterized by more complex failure modes.

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications

AK-SYS-IE: A novel adaptive Kriging-based method for system reliability assessment combining information entropy

Structural reliability assessment is a popular topic in engineering problems, particularly for the larger and more complex systems with implicit performance functions, also called black-box problems. The reliability assessment for a black-box problem must continuously be computed using simulation models, such as the finite element model, a highly time-consuming process with a high computational cost. The adaptive Kriging has gained considerable attention over the past decade. The Kriging-based reliability assessment method reduces the computational cost to a great extent, on the premise of ensuring the accuracy of reliability assessment. However, many of the currently published system reliability assessment methods, construct adaptive Kriging models by reducing the probability of incorrect prediction of the system state, and do not make full use of the uncertainty of the system state prediction information. To this end, a new Kriging-based method for structural system reliability assessment is proposed in this study. First, the probabilities of incorrect or correct system state predictions were understood from the perspective of information entropy. Second, an active learning strategy is proposed based on information entropy theory. Finally, the advantages of the proposed method are demonstrated and highlighted through several numerical examples. The results show that the proposed method achieves a good balance between the accuracy and computational cost, and the numerical magnitude effect does not affect the computational cost. Moreover, this is an effective method for assessing the reliability of complex systems.

Eurocode-compliant system-level reliability analyses of trussed portal frames under climatic loads

Eurocode 3 provides Geometrically and Materially Nonlinear Analysis with Imperfections-method (GMNIA) in which the entire structure can be designed in system-level. In GMNIA, reliability of structural system is verified by using a system safety factor which is obtained in Eurocode 3 based on numerical or experimental capacity test results. Unfortunately, such test results are scarcely published in the literature thus complicating the determination of the factor for a design engineer. In the so-called Direct Design Method, however, system safety factors are provided in advance for the design engineer by system-level reliability studies. This study determines the Eurocode-compliant GMNIA system safety factor for Warren truss portal frames by using the approach of the Direct Design Method. Advanced numerical models are utilized to perform Monte Carlo-simulations for entire structural systems. These simulations provide statistical distributions of system resistances which are employed in the First-Order Reliability Method to derive the system safety factors. Studied structures are made of S700 cold-formed hollow sections. Various system geometries, system configurations and load combinations consisting of snow and wind loads are investigated and a suitable system safety factor is proposed for the ultimate limit state design of Warren truss portal frames. The proposed system safety factor is applied in a practical comparison in which Warren system is designed both by the conventional Eurocode 3 method and GMNIA. This comparison reveals that GMNIA has a remarkable potential to offer reduced material consumption in design compared to the conventional method.

Influence of width-to-depth and effective length-to-depth ratio on shear strength of reinforced concrete slender beams without shear reinforcement: comparative analysis

PurposeFrom the factors that affect shear strength of reinforced concrete (RC) beams, the study examines the effect of controversial parameters, width-to-depth (b/d) and effective length-to-depth (leff/d) ratio on shear strength of RC slender beams.Design/methodology/approachThe researchers utilized a database of 676 experimental test results from ACI-DAfStb database, Conducted regression analysis to examine relationship between b/d and leff/d ratios and shear strength, compare and analyze sensitivity to changes in b/d and leff/d ratios for the selected 12 shear models for RC beams.FindingsIncreasing b/d ratio enhanced shear strength until b/d ˜ 3, but further increases had limited impact and increasing leff/d ratio resulted in decreased shear strength. From comparative analysis, the models provided by various design standards were found to be safe, with EC-2 and JSCE models being conservative. From considered research models, Campione and Arslan models were conservative, while Kim and White model were observed to be unsafe. Sensitivity analysis indicated ACI318-19, JSCE, CEB-FIP-90 and Arslan models were sensitive to changes in b/d and leff/d ratios. National code models generally captured shear strength characteristics well. Certain models suggested a constant/decreasing b/d effect despite observed shear strength enhancement. Most models indicated improved shear strength with an increasing leff/d ratio, contrary to experimental findings while TS500 and Hwang models aligned with experimental results.Research limitations/implicationsThe study's limitations include the dependence on the available database, which may not encompass all possible experimental scenarios. Further research should aim to expand the database and investigate additional parameters that may influence shear strength in RC beams.Practical implicationsThe findings of this study have practical implications for the design and analysis of RC beams by suggesting that the width-to-depth and length-to-depth ratios should be carefully considered to optimize shear strength. The identified models can assist engineers in selecting appropriate shear strength prediction models based on specific design scenarios.Social implicationsThe study contributes to the advancement of knowledge in the field of reinforced concrete beam design, which has implications for the safety and reliability of structural systems. By understanding the factors influencing shear strength, engineers can design more efficient and robust structures, ensuring the safety of buildings and infrastructure.Originality/valueThis study provides valuable insights into the influence of the width-to-depth and effective length-to-depth ratios on shear strength in reinforced concrete beams. It contributes to the understanding of these factors and their impact on shear strength, addressing the lack of consensus among researchers. The comparative analysis of shear models and the sensitivity analyses add value by identifying the models that align better with experimental observations. The study emphasizes the need for accurate models that account for these factors and highlights the importance of further research to refine and develop improved predictive models.

Generative adversarial surrogate modeling framework for aerospace engineering structural system reliability design

To effectively realize the reliability design of engineering structural system, a generative adversarial surrogate modeling (GASM) concept is proposed by innovating generative adversarial theory into surrogate modeling methods, which realize the aerospace engineering structural system reliability design adversarial modeling. In this concept, the surrogate model is adopted as a basis function to describe the functional relationship between input variables and output response; the generative adversarial theory is employed to obtain hyper-parameters of basis functions by continuous confrontation and evolution between the generation model and discrimination model. Under the GASM concept, the generative adversarial polynomial chaos expansion (GAPCE) method is developed to achieve aerospace engineering structural system reliability design. The effectiveness of presented GAPCE is verified by three examples including the nonlinear functions approximation, reliability design of flap deflection angle, and reliability estimation for turbine blisk radial deformation. The reliability level of flap deflection angle and turbine blisk radial deformation are 0.9998 and 0.9984, when the allowable values are 9° and 1.9222 × 10−3 m. Besides, the introduced approach possesses advantages of modeling performance (i.e., modeling accuracy and efficiency) and simulation properties (i.e., simulation precision and efficiency) by comparing with various methods. The efforts of this paper can provide a valuable reference for the operational safety of flap systems and engine structures and the development of reliability design theory.

PDEM-based multi-component and global reliability evaluation framework for steel tubular transmission towers with semi-rigid connections

The conventional analysis and design of transmission towers usually ignore the effect of semi-rigid behaviors of bolted joints and simplify the joints as the rigid or pinned connection, which might overestimate or underestimate the structural response of transmission towers and the reliability of structural components and systems. To consider the effect of semi-rigid connections on the reliability of transmission towers in this paper, a stochastic dynamic finite element model of steel tubular transmission towers with semi-rigid connections (STTTs-SRCs) is established by deriving their stiffness matrix, where the uncertainties of dimensions, material and semi-rigid connections are considered and the wind loads are simulated as stochastic processes by the dimension-reduced probabilistic simulation approach. Then, based on the equivalent extreme-value event, the multi-component and global performance functions with stress and displacement failure modes for STTTs-SRCs are established, respectively. Further, a reliability evaluation framework for STTTs-SRCs is proposed based on the probability density evolution method (PDEM). Finally, a practical engineering transmission tower is regarded as an example to demonstrate the implementation process of the proposed reliability evaluation framework. Moreover, the reliability of steel tubular transmission towers with rigid and pinned connections is compared with that of STTTs-SRCs to investigate the effect of semi-rigid connections on the reliability of transmission towers. In general, the semi-rigid connections would mainly affect the value of the multi-component failure probability, however, it would not change the location where the maximum failure probability of components occurs. Additionally, the semi-rigid connection significantly affects the global failure probability of towers for the stress failure mode while slightly affects the global failure probability of towers for the displacement failure mode. Therefore, it may be more appropriate to consider the semi-rigid connections into the analysis and design of steel tubular transmission towers.

Predicting acceleration response of super‐tall buildings by support vector regression

SummaryRecovering missing data of defective sensors is an important challenge for reliability of structural health monitoring systems and misjudgment of structural conditions. The present study concerns predicting corrupted data of lost sensors by support vector regression (SVR). The method is tuned via optimizing their parameters by observer–teacher–learner‐based optimization as a powerful meta‐heuristic algorithm. Their performances are compared in predicting the acceleration responses of two real‐world super‐tall buildings: Milad Tower, located in Tehran, and Canton Tower in Guangzhou. Also the minimum required of sensors to predict the acceleration responses are investigated. The results are evaluated by five statistical indices exhibiting that the optimized SVR has sufficient capacity to predict acceleration responses of both towers with limited number of sensors. The proposed method is of practical interest as it does not require finite element modeling of the structure to derive its dynamic responses.

Active learning-based structural reliability evaluation Kriging model and sequential importance sampling

The efficient analysis of structural system reliability with multiple failure modes and small failure probabilities presents significant challenges for active Kriging methods. This paper proposes a novel method, PAK-SEIS, which combines the Parallel Active learning Kriging model and the Sequential Importance Sampling method to overcome these challenges. The proposed method includes a new sequential importance sampling method that integrates the sequential Monte Carlo simulation (MCS) and kernel density estimation to construct the optimal importance density. Additionally, the proposed parallel learning strategy allows for the selection of multiple new training samples, reducing performance function calls and refining iterations of Kriging models. The PAK-SEIS method gradually approximates the optimal importance density by updating multiple new training samples and failure modes in parallel at each iteration based on a small number of candidate samples. Candidate samples generated in the final sequence can cover all limit state surfaces, and all Kriging models are accurately constructed. The PAK-SEIS method can effectively address system reliability problems with small failure probability, multiple failure regions, and implicit functions. Two examples demonstrate the efficiency and accuracy of the proposed PAK-SEIS method.

Wavelet-Based Output-Only Damage Detection of Composite Structures.

Health monitoring of structures operating in ambient environments is performed through operational modal analysis, where the identified modal parameters, such as resonant frequencies, damping ratios and operation deflection shapes, characterize the state of structural integrity. The current study shows that, first, time-frequency methods, such as continuous wavelet transform, can be used to identify these parameters and may even provide a large amount of such data, increasing the reliability of structural health monitoring systems. Second, the identified resonant frequencies and damping ratios are used as features in a damage-detection scheme, utilizing the kernel density estimate (KDE) of an underlying probability distribution of features. The Euclidean distance between the centroids of the KDEs, at reference and in various other cases of structural integrity, is used as an indicator of deviation from reference. Validation of the algorithm was carried out in a vast experimental campaign on glass fibre-reinforced polymer samples with a cylindrical shell structure subjected to varying degrees of damage. The proposed damage indicator, when compared with the well-known Mahalanobis distance metric, yielded comparable damage detection accuracy, while at the same time being not only simpler to calculate but also able to capture the severity of damage.

An improved active Kriging method for reliability analysis combining expected improvement and U learning functions

The reliability assessment of structures with multiple failure modes and small failure probability is challenging due to the time-consuming simulations required. Active learning Kriging methods for structural reliability with multiple failure modes have shown high computational efficiency and accuracy. However, selecting the appropriate sample and its failure mode to update the Kriging models remains a key problem. In this paper, we propose a new learning function and stopping criterion to further improve the efficiency of structural system reliability analysis. Firstly, we propose a new learning function that combines the expected improvement function and the U learning function. This function selects the most suitable samples, balancing the degree of expected improvement of samples to the limit state surface and the degree of misclassification probability of samples. Secondly, we propose a new stopping criterion that considers both the accurate construction of limit state surfaces and the probability of accurately predicting the signs of samples. This criterion avoids premature or late termination of the active learning process. Thirdly, the sequential MCS simulation method is employed in the active learning process to efficiently evaluate small failure probability problems. By analyzing four examples, we verify the accuracy and efficiency of the proposed structural reliability analysis method.

General partial safety factor theory for the assessment of the reliability of nonlinear structural systems

The paper develops a novel and general theory for characterizing the nonlinearity of structural systems and for applying partial safety factors to these systems. The theory establishes a key relationship between the partial safety factor concept and the reliability theory of nonlinear structural systems, using the degree of homogeneity as a measure of nonlinearity at the design point. This measure allows for an efficient mathematical decoupling of the reliability index into nonlinearity-invariant partial reliability indexes. This formulation enables the identification of critical safety situations in extreme cases of nonlinearities in complex nonlinear structural systems. The theory leads to two main outcomes based on the asymptotic behavior of the reliability index. First, the reliability index of any nonlinear structural system is always bounded between an upper and lower bound, which can be determined using the concept of nonlinearity-invariant partial reliability indexes. Second, nonlinearity-invariant critical partial safety factors ensure that the reliability index is greater than the target reliability index. The proposed theory provides a simple and easy-to-implement method for assessing the safety of nonlinear structural systems, which can be used in conjunction with existing codes and standards. The theory can be applied to a wide range of nonlinear systems in engineering practice, including structural and geotechnical problems. It is designed primarily to provide code writers with the necessary procedure for calibrating partial safety factors for nonlinear structural systems and to identify the over-safe or under-safe cases.

Seismic behaviour of a novel demountable joint connecting FTCES columns and RC beams

Fiber-reinforced polymer (FRP) tube-concrete-encased steel (FTCES) column shows excellent strength, stiffness and ductility owing to composite action. However, its practical application is very limited because there is a lack of knowledge on the behaviour of the entire structural system, which includes the beam-column joint. Indeed, the design of the connection between FTCES columns and beams is the most challenging task, which governs the reliability and safety of the entire structural system. To fill up the research gap, a novel demountable beam-column joint was proposed to connect the FTCES columns and reinforced concrete (RC) frame beams using high-strength bolts. The joint consisted of two cover plates, two steel ring plates, two connecting beams (with end plates) and a hollow steel tube infilled with core concrete. A total of four specimens were designed to study the seismic behaviour of the proposed joint, considering the effect of the steel tube / plate (ring plate and flange of connecting beams) thickness and the axial load ratio. Test results showed that the proposed demountable joint could provide an effective and simple connection with favourable seismic performance. Besides, with thinner steel tube and steel plate, specimen failed due to steel fracture in the joint region and few minor cracks were observed on the RC beams. Under this circumstance, the material strength of RC beams was not fully utilized, leading to smaller peak load. For specimens with thicker steel tube and steel plate, the failure was due to serious concrete crushing on the RC beams and excellent seismic performance was achieved. Lastly, an analytical model was proposed to estimate the peak load of the joint and by comparison, it is concluded that the proposed model could give a very accurate prediction.

Multivariate ensembles-based hierarchical linkage strategy for system reliability evaluation of aeroengine cooling blades

To improve the computing accuracy and efficiency of system reliability evaluation for aeroengine cooling blades, by fusing the benefits of multivariate ensembles model (ME) into the hierarchical linkage technique (HL), a multivariate ensembles-based hierarchical linkage strategy (ME-HL) is proposed. In the ME-HL modeling, the complex evaluation system is first decomposed into multiple subsystems (i.e., frail site and failure mode) by the developed HL strategy, after that the multiple output responses of subsystems are synchronously mapped by proposing the ME model, and the multi-level system reliability framework is finally built with the Copula-based correlation quantification. The reliability evaluation of a typical aeroengine turbine cooling blade is regarded as a case, to verify the effectiveness of the proposed strategy. From the reliability evaluation results, we observe that the acquired reliability degree considering the failure correlation (i.e., 0.9547) is higher than that of without considering the failure correlation (i.e., 0.9356). From the methods comparison, we discover that the computing efficiency of the ME-HL method is 86.89%, 80.12%, 80.63%, and 18.85% greater than that of the ANN, BT, ME, and BT-HL methods, respectively; and the computing efficiency of the ME-HL method is 12.47%, 11.26%, 6.11%, and 0.93% higher than that of the ANN, BT, ME, and BT-HL methods, respectively. The evaluation and comparison results demonstrate that the proposed ME-HL holds significant advantages in computing accuracy and efficiency in system reliability evaluation problems. The current study can shed a light on the complex multi-level structural system reliability evaluation.

Analysis on Reliability of Wind Barrier on High-speed Railway Bridge under Wind Load

In order to guarantee the safety of wind barrier structure on the railway bridge and to prevent the overturning of wind barrier in the strong wind impacting on the normal operation of the train, the study on reliability of wind barrier is conducted. Taking the single side 4m wind barrier installed on the Lanzhou-Xinjiang second double-line railway bridge as an example, considering the randomness of natural wind velocity and train speed, based on the probabilistic theory analyze the reliability of the wind barrier structure system under the static wind load and dynamic wind load, and evaluate the wind barrier safety with the reliability index and failure probability of structural system. And parameter sensitivity analysis is performed to find out which factors are more important to the reliability of the wind barrier system. The research results show that the reliability index of the wind barrier system meets the requirements of the design specification, and the failure probability is small; the reliability index of the wind barrier system under the static wind load and dynamic wind load is more sensitive to the wind velocity than the train speed.

Effect of System Reliability on Design of Metal Building Roof Purlins

This paper provides a framework to incorporate structural system reliability effects in the design of roof purlins in a typical metal building. Today, every roof purlin is considered as a separate component, and the effect of spatial variation in the demand loads and potential redistribution and load sharing in the roof system capacity is ignored in design. Component reliability is established by first-order reliability methods implemented through load and resistance factor design. Based on recent work in load-bearing cold-formed steel framing systems, the load and resistance factor design framework is extended from components to systems through an additional resistance factor to account for system influence. An archetypical metal building is designed and selected for this study. Monte Carlo simulations of a segment of the metal building roof are performed with consideration of both randomness in the demands and capacity and employing geometric and material nonlinearity in the response model of the roof. The simulations indicate that the system effect in metal building roofs is beneficial, and increases in the design capacity when evaluated against demands may be justified. Sensitivity to the target reliability (allowed probability of failure), deflection limits, and modeling assumptions are observed and discussed. Preliminary factors to account for roof system reliability are provided.

An efficient algorithm for analyzing multimode structure system reliability by a new learning function of most reducing average probability of misjudging system state

• A new MRF learning function for multimode structure system is proposed. • A contribution function is established and explicitly derived. • A reasonable convergence criterion is employed. • The accuracy and efficiency of the proposed method are verified by five examples. For efficiently analyzing reliability of multimode structure system by adaptive Kriging model combined with Monte Carlo simulation, a new most reduction learning function (MRF) is proposed in this paper. Firstly, a contribution function is established to quantify the effect of candidate sample point (CSP) on reducing average probability of misjudging single mode structure state in the differential domain adjacent to CSP. Secondly, the contribution function is explicitly derived after selecting a reasonable size of the differential domain. Thirdly, by the maximum expected contribution function of single mode structure and the logical relationship among modes in multimode structure system, the new learning function named MRF is obtained. The main novelty of this work is proposing a new MRF based strategy for multimode structure system reliability analysis, in which it considers the logical relationship among modes in multimode structure system, probability of misjudging single mode structure state, probability density function and the absolute value of Kriging prediction of single mode structure at CSP. Compared with the up-to-date methods, the proposed strategy for multimode structure system reliability analysis is easier and more efficient to be executed, and its superiority is verified by numerical and engineering examples.

Using System Reliability Concepts to Derive Partial Safety Factors for Punching Shear with Shear Reinforcement: An Explorative Analysis

Punching shear-reinforced flat slabs can fail in three modes: (1) inside the shear-reinforced zone, (2) outside the shear-reinforced zone, or (3) at the maximum punching shear capacity. By using the theory of structural systems reliability, this study investigates how these failure modes combined affect the safety level of reinforced concrete flat slabs with punching shear reinforcement, and ultimately, the variation of partial factors. Based on DIN EN 1992-1-1 together with the German National Annex DIN EN 1992-1-1+NA(D), and by using distinct reliability-based methods (i.e., levels II and III), the variation in the system failure probabilities and, consequently, partial factors were analysed. The results indicated that partial factors can fluctuate significantly depending on the governing ultimate limit state function and the scatter of basic variables. Furthermore, they indicated that the chosen reliability-based method had a significant influence on the resulting safety level. The sensitivity analysis confirmed the premise that partial factors can be reduced without compromising socially accepted safety levels (e.g., recommended in DIN EN 1990). Ultimate benefits include material and cost savings and CO2 emission reductions. Finally, the approach addressed in this paper offers a new perspective on the derivation of partial factors that can be considered by engineering practitioners.

A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis

The reliability analysis system is currently evolving, and reliability analysis efforts are also focusing more on correctness and efficiency. The effectiveness of the active learning Kriging metamodel for the investigation of structural system reliability has been demonstrated. In order to effectively predict failure probability, a semi-parallel active learning method based on Kriging (SPAK) is developed in this study. The process creates a novel learning function called UA, which takes the correlation between training points and samples into account. The UA function has been developed from the U function but is distinct from it. The UA function improves the original U function, which pays too much attention to the area near the threshold and the accuracy of the surrogate model is improved. The semi-parallel learning method is then put forth, and it works since UA and U functions are correlated. One or two training points will be added sparingly during the model learning iteration. It effectively lowers the required training points and iteration durations and increases the effectiveness of model building. Finally, three numerical examples and one engineering application are carried out to show the precision and effectiveness of the suggested method. In application, evaluation efficiency is increased by at least 14.5% and iteration efficiency increased by 35.7%. It can be found that the proposed algorithm is valuable for engineering applications.

Stability of antenna support belts

Introduction. The article deals with the issues of stability of compressed belts of lattice construction, as systems in the form of continuous rods consisting of panel elements. Currently, the estimated length of compressed elements is established by design standards for calculating stability without taking into account variability due to the influence of numerous random and systemic factors. This approach does not encourage the improvement of design solutions, the quality of their execution and the control of the structure. And most importantly, it does not allow to realize the reserves of the bearing capacity of structures laid down by the norms. The purpose of this study is to identify and substantiate the reserves of the bearing capacity of antenna supports of a lattice structure according to the criterion of stability of compressed belts.Materials and methods. It is shown that the values of the calculated length coefficient are set by norms with a margin, due, in particular, to ensuring the reliability of structural systems of compressible elements. It is proposed to consider the estimated length of compressed elements as a random variable. The problem of probabilistic estimation of the free (calculated) length of a centrally compressed element as a parameter of the critical force of loss of stability is solved. The possible normative and calculated values of the calculated length coefficient are given, ensuring the reliability of the calculation of compressed antenna support belts, adopted in the current design standards. To assess the variability of stability indicators, the task is to conduct experimental studies, in particular, with the measurement of vibration frequencies of full-scale structures. By controlling the variability indicators of the calculated length coefficient, it is possible to regulate not only reliability, but also the load-bearing capacity reserves provided for by design standards.Conclusions. The proposed approach to the calculation of stability allows to control the calculated values of the calculated length coefficient by controlling statistical variability (coefficient of variation). If there are available methods for measuring indicators, the approach described in the article to calculating compressed elements for stability can be an effective means and tool for improving design solutions, the quality of their execution and control of the structure.