Steel Truss Structure Research Articles

Global Sensitivity Analysis and Surrogate Models for Evaluation of Limit States in Steel Truss Structures

This article presents the global sensitivity analysis of the serviceability limit state of a steel truss using Monte Carlo simulations. The focus is on the probabilistic assessment of deflection, with failure probability defined as the likelihood of exceeding the deflection limit. Deflection is computed using the beam finite element method. A surrogate model is introduced to reduce computational costs. By integrating the surrogate and original models, significant CPU cost reductions are achieved. Furthermore, classical Sobol sensitivity analysis is used to examine the model outputs and analyze the significance of member loading and stiffness on the deflection. This study advances the use of surrogate models in global sensitivity analysis, enhancing computational efficiency and the understanding of interactions between input variables in the reliability assessment of steel truss structures.

Research on the Gradual Change Model of Vibration Characteristics of Spatial Steel Truss Structures Under Fatigue Loading

Research on the Gradual Change Model of Vibration Characteristics of Spatial Steel Truss Structures Under Fatigue Loading

A demand-capacity approach to define failure thresholds in anomaly detection monitoring systems

This work proposes a procedure to establish the threshold for a structural anomaly detection system using a supervised computation of the demand-capacity ratio (DCR). A truss structure is analyzed with a large dataset of damage scenarios generated through Monte Carlo simulations, varying in types and intensities of damage. For each scenario, a structural model is subjected to ultimate loads to calculate the DCR for each element, as the ratio between the demand forces and the capacity of the element. Scenarios where the DCR of any element exceeds one, are identified as those potentially leading to failure. For these scenarios, a dataset of pseudo features sensitive to damage, such as modal frequencies, is created, incorporating environmental and operational variations, as well as noise. A damage index is then constructed for each damage scenario as the Mahalanobis distance between the damaged and the healthy modal frequencies datasets. The alarm threshold is finally set at the value exceeded by the damage indexes of all scenarios leading to failure. The method is demonstrated with a numerical example of a steel truss structure under a specific load, considering 100,000 damage scenarios. Results show that the proposed threshold is conservative, not easily derived from simple engineering intuition, and effectively minimizes false alarms in the detection scheme.

Identification of Bolt Loosening Damage of Steel Truss Structure Based on MFCC-WPES and Optimized Random Forest

In the field of bolt loosening detection, although some progress has been made, there are still challenges such as high operational complexity, single feature extraction methods, and insufficient analysis model performance, especially in large steel truss structures, where there is a lack of efficient and accurate bolt loosening identification solutions. In response to these shortcomings, this article proposes an innovative bolt loosening damage recognition method based on sound signals. This method integrates feature extraction techniques of Mel frequency cepstral coefficients (MFCCs) and wavelet packet energy spectra (WPES), and comprehensively characterizes sound signals by constructing MFCC-WPES combined features. Subsequently, the random forest (RF) algorithm optimized by genetic algorithm was used for feature selection and model training, aiming to improve recognition accuracy and robustness. The experimental results show that this method can not only accurately identify bolt loosening signals in steel truss structure bolt loosening detection, but also has strong identification ability for environmental noise. Compared with traditional methods, the proposed solution in this article shows significant improvements in both performance and practicality, providing a new perspective and solution for the technological advancement of bolt loosening detection in steel truss structures.

Study on updating finite element model of steel truss structure based on knowledge-enhanced deep reinforcement learning

Accurate assessment of the damage to a structure is the key to structural health monitoring. Among them, one effective means to assess the real-time status of the structure is by establishing a finite element model (FEM) of the structure and updating it synchronously. Previous research mainly updates the FEM of the structure with a certain moment in time without considering the continuous changes of structural parameters over a longer time. In this paper, we propose a FEM updating method for steel truss structure based on knowledge-enhanced deep reinforcement learning, which introduces long short-term memory (LSTM) network based on a deep deterministic policy gradient (DDPG) algorithm and constructs a knowledge-enhanced noise function (NF) module based on parametric analysis. The NF-LSTM-DDPG algorithm can fit a continuous variation of structural parameters. The ablation experiments revealed that the LSTM network and the NF module facilitate the exploration and learning ability of the DDPG algorithm. The NF-LSTM-DDPG algorithm is used in an actual steel truss structure case, and the variation rules of the elastic modulus and support deviations obtained from the solution are consistent with the actual situation. The average relative error of the 34 strain measurement points after updating is 14.147 %, and the relative error is higher than 20 % in only 13 days out of 187 days of continuous monitoring. Five days were selected for the comparative study, and the experimental results show that the relative error of the NF-LSTM-DDPG algorithm is 13.206 %, which is higher than that of the particle swarm optimization algorithm and the simulated annealing algorithm. The change of elastic modulus updated based on the NF-LSTM-DDPG algorithm is more reasonable than other algorithms.

Virtual trial assembly of large steel members with bolted connections based on multiscale point cloud fusion

AbstractVirtual trial assembly (VTA) using 3D laser scanning as the digital carrier can overcome the shortcomings of time‐consuming and costly physical preassembly. However, its application in large steel structures with bolted connections remains limited. First, this study introduces a novel approach for acquiring multiscale point cloud data of large steel members using terrestrial laser scanners (TLSs) and hand‐held scanner (HHS). This approach considers both the global data and the local details of the steel members. Additionally, a precise registration method based on magnetic 3D targets is proposed for multiscale point clouds, which enables the registration accuracy of multisource point clouds to reach submillimeter precision. Subsequently, a novel algorithm for feature point screening is introduced, which utilizes a dichotomous point cloud grid approach to identify and extract a significant quantity of bolt holes effectively. This approach enables fully automated and fast extraction of the point cloud on the cylindrical inner surface of the holes. Finally, the bounding box and Procrustes analysis approach are employed to perform VTA using the point cloud of the cylindrical bolt holes as the assembled features. The accuracy and feasibility of the above method are verified by a down‐scale modeling experiment and project test, which provide technical support for the VTA of large steel truss structures.

Damage Identification of Full-Scale Steel Truss Structure Based on Model Condensation and Mean-Value Normalization Regularization Techniques

Structural health monitoring and damage identification aim to detect the internal damage and evaluate the health conditions of the practical engineering structure, which has been the most popular research field for several decades. The sensitivity-based method incorporated with the regularization techniques is the classical and useful approach, and it can obtain accurate damage detection results. However, with the development of civil engineering structures, this classical method faces two problems: one is it is only applied to simple structures rather than full-scale structures, and second is the iterative calculation efficiency is lower. Therefore, aiming at these drawbacks, the two improvement strategies have been introduced to the original method for its enhancement in the application potential and computational efficiency. The proposed method has been verified based on two examples, i.e., a numerical steel truss with 144 elements and a full-scale experimental steel truss with 160 elements. The results prove that the proposed method has better efficiency and good application potential in the practical full-scale engineering structure.

Experimental study and finite element analysis on impact resistance of wind turbine steel truss foundation

In recent years, steel truss structures have been widely used in civil engineering and wind turbines. In addition to bearing vertical loads, they are also vulnerable to impact accidental loads. If the foundation structure undergoes large deformation, it will lead to the overall instability and collapse of the structure. In this paper, the impact load resistance of steel truss foundation of wind turbine under eccentric mass is taken as the research object, and the combination of pendulum impact test and finite element simulation is adopted. The impact resistance of steel truss foundation was analyzed from the parameters of failure characteristics, impact response and energy dissipation capacity through multiple pendulum impact tests. The results show that after two impacts the stiffness of the impact leg is significantly reduced and even fracture at its root. The branch tube near the point of impact are squeezed and bent, and the deformation and acceleration peak of the impact position are the largest. The denting of the impact leg is the main energy dissipation mechanism. These data provide an experimental basis for improving the impact resistance of full-scale wind turbine structures.

Shaking table test on seismic performance of a large-span high-rise building

This paper describes investigations in respect of the seismic performance of a large-span high-rise building in a mountainous area. The building consists of a 135 m high shear wall structure and a 174.5 m long steel truss structure, with dampers used to enhance the seismic performance. A 1/40 scale model of the prototype structure was designed, and shaking table tests was conducted. The experiments simulated the wave passage effect and slope amplification effect based on the building site and structural characteristics of the prototype structure. The seismic performance of the prototype structure was analyzed through the damage phenomenon, dynamic characteristics, and dynamic response of the model under earthquake effects. The results show that three seismic waves were delayed by about 0.4 s and amplified by about 1.6 times after passing through the steel frame with viscous dampers, which could effectively simulate the wave passage effect and slope amplification effect in the test. The maximum story drift ratios of the model shear wall structure and steel truss structure were 1/1258 and 1/455 for the SLE and 1/568 and 1/185 for the MCE. The damping devices played a key role in energy dissipation. As a result, this research provides a reference for the seismic design and shaking table testing of large-span high-rise buildings.

Extraction of Feature Information from Point Cloud of Large Volume Steel Truss Members

In order to address the issues pertaining to the subjective nature and limited precision associated with selecting feature points in the point cloud of a large steel truss structure, this study proposes a batch automatic extraction approach for identifying key feature information, including boundaries, corner points, and bolt holes of large steel truss components. This method relies on the nested application of established processing algorithms such as Euclidean clusters, regional growth clusters, and random sampling consensus. In addition, a novel approach is suggested for validating the precision of feature information extraction through the utilization of standard theoretical models. The results of the experimental and large-scale lower chord tests demonstrate that our approach is not dependent on specialized software, exhibits excellent efficiency, and possesses an acceptable degree of automation. The findings of this study can provide accurate data support for reverse modeling, virtual trial assembly, and dimensional inspection of steel truss components.

Gradual change of vibration characteristics of steel truss structure

Abstract In order to investigate the gradual change law of vibration characteristics of steel truss structures under the influence of fatigue loading and uniform corrosion, this study combines the nonlinear cumulative fatigue damage rule, corrosion depth theory, and structural residual performance theory to establish a multi-level gradually changing performance transfer model. The relationship between structural performance and damage behavior is revealed, and a numerical analysis method for the gradual change phenomenon of vibration characteristics of steel truss structures is provided. A two-dimensional finite element analysis framework for gradual changes in vibration characteristics in steel truss structures has been developed and analyzed for a steel truss girder bridge. The results show that the correlation terms of vibration characteristics gradually decay with the increase of operation time, and the degree of decay is related to the severity of fatigue damage or corrosion damage, with the overall trend exhibiting a three-stage change.

Safety assessment and retrofitting of a historic double deck riveted steel bridge

The problem of managing existing bridges on the national territory is now extremely topical, considering that most of these works have exceeded their design life and that traffic on its roads and rail networks has increased significantly over the years, both in terms of flow and load intensity. The case study, it is a double deck riveted steel bridge, the lower one dedicated to railroad traffic and higher one to road traffic, dating back to two different eras since the masonry substructures were built towards the end of the 19th century, while the steel truss was replaced after the Second World War. It consists of a single steel truss structure with three spans, two of which have lateral span of 82.40 m and central one of 99.00 m, for a total length of 263.80 m. The study aims to evaluate and ensure the safety of the work in accordance with current code (NTC2018-RFI Manual) by introducing reinforcement systems on the steel truss bridge that increase its strength while preserving its original architectural appearance. The static and dynamic structural analyses were carried out by defining a finite element model (FEM), within which different load conditions and positions between the railway and road deck were implemented, assessing their contemporaneity, in order to maximize the stresses. The reliability of the implemented numerical model was demonstrated by a calibration process, following specific load tests, with displacements reasonably comparable with the experimental results. The safety level of both the existing conditions and the project has been defined through the implementation of VBA scripts and automated spreadsheets, made on an ad hoc basis, which allow the development of all the checks on the structural elements of non-standard sections, i. e. composite riveted sections. In assessing the safety index, it was taken into account the state of conservation of the structure and of the transitional stages in the implementation of the reinforcement systems.

Seismic Analysis of Large-span Steel Truss Structure Based on PKPM

In the construction industry, large-span steel truss structures are widely used. In order to test their seismic performance under different structures, select truss structures with better seismic performance, and use PKPM software to establish two cross-sectional triangular arch truss models with different web member arrangements, one cross-sectional trapezoidal arch truss model. Using the mode decomposition response spectrum method, simulate and analyze the displacement and stress changes of three sets of finite element calculation models under different load combinations during earthquakes. The results showed that all truss models meet seismic requirements. The change in the arrangement of the web members did not significantly improve the seismic performance of the cross-sectional triangular truss, the seismic performance of the cross-sectional trapezoidal arch truss was significantly better. The lower chord of the triangular arch truss support and the mid-span top chord are under compression, while the top chord is only compressed on one side and tensioned on the other side. The top chord of the trapezoidal arch truss is mainly under pressure.

Temperature Response of Double-Layer Steel Truss Bridge Girders

Double-layer steel truss continuous girders are prone to significant temperature stress, deviation, torsion, and warping, thus causing adverse temperature structural responses, and also affecting the safety and durability of bridge structures. This paper presents an investigation on time-dependent characteristics in the temperature field and temperature response of double-layer steel truss continuous bridge girders, fully considering the shielding effect subjected to different solar radiation angles during the high-temperature season. The time-dependent thermal boundary conditions and support conditions provided for the steel truss bridge structure were determined. Subsequently, a thermal analysis model for the entire structure of double-layer steel truss continuous girders was established to attain the temperature distribution law. The research results show that significant differences occur in the position and temperature difference of temperature gradients exhibited in the vertical, horizontal, and longitudinal directions in the double-layer steel truss bridge structure. The temperature distribution pattern within the chord section is mainly influenced by the environmental temperature and solar radiation intensity, along with the heat exchange between different panels. Thereafter, a validated temperature gradient formula for the component section has been proposed. The time-dependent laws in structural displacement, stress, and rotation angle under daily temperature cycling conditions have been revealed, thereby providing a theoretical basis for the life cycle construction and safety maintenance of double-layer steel truss structure bridges.

Research on the Identification of Bridge Structural Damage Using Variational Mode Decomposition and Convolutional Self-Attention Neural Networks

Convolutional neural networks (CNN) are widely used for structural damage identification. However, the presence of environmental disturbances introduces noise into the acquired acceleration response data, impairing the performance of CNN models. In this study, we apply empirical mode decomposition (EMD) and variational mode decomposition (VMD) to denoise the data from a steel truss bridge. By comparing the smoothness and convergence of the obtained modal functions (IMFs) using EMD and VMD, we confirm the effectiveness of VMD in smoothing and denoising the bridge structure signals. Additionally, we propose a convolutional self-attention neural network (CSANN) model to extract features and identify damage in the denoised data using VMD. Comparative analysis of the CNN, LSTM, and GRU models reveals that the VMD-CSANN model outperforms the others in terms of damage localization and identification accuracy. It also exhibits excellent performance when handling noise-contaminated data with a noise level of 10%. These findings demonstrate the efficacy of the proposed method for identifying internal damage in steel truss structures, while maintaining smoothness and robustness during processing.

Analisis Profil Atap Baja WF dengan Metode LRFD Menggunakan SAP 2000 dan Idea StatiCa

The roof is one of the main components in a building to protect it from heat and rain. The roof is also an architectural component that adds to the beauty of a building. Therefore, every building must have a roof. Currently, many steel roofs are used as the structural frame. Considering that wooden materials are considered expensive to use nowadays. Apart from that, steel material has a longer durability than wood material, so steel is considered the best alternative at the moment. The title of this article is "Structure Analysis of WF Steel Profile Roofs Using the LRFD Method". This analysis was carried out to determine the strength of the WF steel truss structure and its connections. The analysis method used in this research is the LRFD method which is carried out with the help of the SAP 2000 v20 program to calculate the statics of the roof construction and using the help of the Idea StatiCa program to calculate the connections. Based on the results obtained, the trusses that use the WF 200.100.5,5.8 profile are able to withstand the forces acting on the truss structure, and the connections are sufficient to withstand the forces that occur.

FAST-AlertNet: Early warning fire-induced collapse of large-span steel truss structures

Large-span steel trusses are often adopted as roof structures of public and industrial buildings. Besides significant property loss, the unexpected and sudden collapse of large-span steel trusses caused by fires also threatens the lives of firefighters and trapped people in fires. To prevent casualties to the greatest extent, real-time evaluation of the structural collapse state of the building at fire rescue scenes is critical. This paper presents a novel framework for early warning the collapse of large-span steel truss structures in fire based on proposing the FAST-AlertNet. The FAST-AlertNet can easily obtain real-time displacements based on easily-measured rotations and temperatures of the steel trusses during fire synchronously. Rotations and temperatures, which reflect uncertain structural parameters of steel trusses and fire scenarios, serve as inputs, while displacements are the outputs. A dynamic weighted loss function is employed in the FAST-AlertNet, yielding better performance reflecting the early-warning points than traditional loss functions. To address the high computational cost of obtaining real spatiotemporal patterns of temperature development inside the building in real fire scenes, transfer learning is utilized to transfer the interlinkage among displacements, temperatures, and rotations established by simplified parametric temperature curves, resulting in improved prediction accuracy in reality. A case study demonstrates the availability of the proposed framework, with the predicted remaining pre-collapse time of a typical steel truss satisfactorily matching the real value. Notably, the proposed framework provides a new methodology for early warning building collapse without requiring the difficultly-measured displacements in fire, which advances its feasibility for practical application.

Additive manufacturing of steel components by cold spraying

AbstractDesign of steel structures in the construction industry uses built‐up or rolled steel sections. These are processed as steel elements and connected together at joints. Massive and complex steel nodes such as those in steel frame bridges or truss structures are still manufactured by forging or casting to avoid weld seams in highly stressed areas. Since this is a manual manufacturing process, it results in increased costs and time. Cold spray (CS) technology has recently attracted attention in aerospace and automotive engineering for producing metallic, composite coatings as well as repairing the surface damage of components in service. Additive manufacturing by cold spraying can offer significant advantages with respect to other additive manufacturing methods due to higher material deposition rates (up to 14 kg/h), lower energy consumption, and deposition in solid state. So far cold spraying has not been used for 3D‐printing of typical steel constructions.In the preliminary test campaign, structural steel‐powder was investigated to evaluate their suitability for cold spraying in civil engineering applications. Simple flat specimens were used, and a range of metallographic and mechanical tests were conducted to determine the properties of the deposited coupons, such as the coating quality, layer thickness, the degree of porosity, micro‐hardness, and micro flat tensile test. This paper presents the results of the powder characterisation and the preliminary tests on the printed specimens.

Block Sliding Construction Technology of Large-span Prestressed Steel Truss Structure

This work, based on the construction process of large-span prestressed steel truss structure roof of Weifang Medical College as an example, used the load test calculation and construction process simulation analysis method to study the prestressed steel truss roof structure, ground single truss assembly, double machine lifting, string cable pretension, truss composition unit, unit block sliding, construction process force, deformation, and technologies. Under the constraints of the site, machinery, construction period and other conditions, the problem of large span and deep components was solved. It not only contributes to ensuring the quality and safety of the project structure but also makes all kinds of work operate simultaneously, which is conducive to shortening the construction period.

Photogrammetry-based bending monitoring and load identification of steel truss structures

The bending of steel truss structures is an important gauge for detecting, identifying, and evaluating potential issues with structural safety performance. The limitations and high cost of traditional monitoring methods make it challenging to carry out stable long-term monitoring. Therefore, this paper developed a displacement monitoring system for steel truss structures which fulfill the requirements of having low cost, high stability, and ease of operation. The system is based on the improved sub-pixel positioning technology, achieving precise positioning in unfavorable conditions such as long structure-camera distance, angle skew, and dim light. Then, this system was calibrated through field experiment and compared with other measurement systems. Finally, a load identification method was developed to identify discrepancies between the true load and the design load. This method uses optimization functions to identify the true load applied in the experiment, and the optimization parameter obtained by a genetic algorithm iteration is output as the optimal solution. The results suggest that the photogrammetric system performs well in practical engineering applications and can provide advantages including high precision, low cost, simple operation, etc. Results obtained by the load identification method agree well with measurements obtained from the actual structure, and can serve as a tool for evaluating the mechanical properties of similar structures. This method monitors potential risks of steel truss structures, and greatly improve the stability and safety of such structures.