Steel Truss Bridge Research Articles

Optimization of steel truss bridge

Optimization of steel truss bridge

Experimental and numerical analyses on stress concentration factors of concrete-filled welded integral K-joints in steel truss bridges

A novel fully welded concrete-filled integral K-joint incorporating longitudinal perfobond ribs welded to the inner faces of the chord is proposed to improve the fatigue behavior. To evaluate the stress concentration factor (SCF) of such joints, a 1:5 scale truss specimen was fabricated and tested to measure the SCF distribution in the fatigue prone joint. A finite element model was also developed to evaluate the SCFmax of concrete-filled welded integral K-joints and was validated against the test results. A parametric study was carried out and a comprehensive database was obtained considering the effects of brace width-to-chord thickness ratio (2γ), brace-to-chord thickness ratio (τ), the angle between braces and chord (θ) on the SCFmax. Also, the following three basic loading conditions were examined: brace axial force, chord axial force and chord in-plane bending. Based on the results of 192 numerical models, new equations to predict the SCFmax of concrete-filled welded integral K-joint were derived for design purposes. Finally, the fatigue behavior of three different designs is compared in terms of SCFmax and hot spot stress range Sh-N curves. These systems are rectangular hollow section (RHS) K-joints, concrete-filled rectangular hollow section (CFRHS) K-joints and concrete-filled welded integral K-joint. It was shown that the concrete-filled integral welded K-joint has the best fatigue behavior, followed by the CFRHS K-joint, while the RHS K-joint has the poorest fatigue behavior.

ASSESSMENT ANALYSIS OF THE CONDITION OF THE SEI. LALAK II BRIDGE USES FRACTURE CRITICAL MEMBER

Bridge inspection plays an important role as prevention before the occurrence of continuous damage. Sei Bridge. Lalak II is a steel truss bridge located on the Airmolek Subdistrict, Indragiri Hulu Regency, Riau Province. The purpose of this study was to analyze the condition of the damage to the elements of the Sei bridge. Lalak II in Indragiri Hulu Regency. The method used to analyse was the Fracture Critical Member (FCM) method. In the results of the FCM method were trusses that had the highest stress ratio in rod number 2 with a stress ratio value (0,717), rod number 7 with a stress ratio value (0,717) and rod number 33 with a stress ratio value (0,717). From this value, the condition of the truss is in the warning category, so light rehabilitation is needed, such as painting the steel frame. At the FCM screening stage, the score obtained was 25 points, then the next examination was carried out a maximum of the next 24 months. At the FCM assessment stage, the score obtained was 79 points, then the next examination will be carried out in the next 6 months. The overall condition of the Sei bridge. Lalak II required light rehabilitation to maintain the bridge in the long term.

Fatigue Performance Evaluation of K-Type Joints in Long-Span Steel Truss Arch Bridge

The K-type joint, which consists of the web members and the chord members with varied angles welded together, has been widely adopted in long-span steel truss bridges. However, its fatigue performance has been rarely considered, despite its critical role in bridge structural safety and durability. Accordingly, the FE model of the K-type joint was established in Abaqus and the fatigue performance analysis was conducted, in which the effect of web/chord thickness ratio (τ), chord/web angle (θ), and chord with rib stiffener were investigated. Take the Mingzhu Bay steel truss arch bridge as an engineering background, the hot spot stress method was employed to calculate the fatigue performance of three K-type joints in unfavorable locations. Furthermore, a 3D full-scall bridge model was built to evaluate the fatigue performance of the K-type joints under standard and overloaded moving vehicle load scenarios. The results show that the max hot spot stress factor (SCFmax) of the web and chord member is influenced by τ and θ. The chord members added stiffener is founded to be an effective way to enhance fatigue performance. The fatigue stress intensities of the three unfavorable locations meet the Eurocode 3 specification requirements, but the one in the mid-truss arch is not satisfied under an overloaded vehicle loading rate of 25%.

A Generalized Framework for the Alternate Load Path Redundancy Analysis of Steel Truss Bridges Subjected to Sudden Member Loss Scenarios

Owing to their distinct features, such as structural simplicity and excellent load-carrying capacity, steel truss bridges are widely applied as critical transportation networks. However, many previously designed steel truss bridges that are located in harsh environmental conditions are facing severe challenges of overloading and aging or deteriorating critical structural members due to increasing traffic load and corrosion problems (i.e., chloride-induced corrosion). As a consequence, these overloaded and deteriorated critical members may initialize localized damage (e.g., sudden member failure scenarios) and then trigger a disproportionate collapse (i.e., progressive collapse) of steel truss bridges. To this end, to protect the steel truss bridges from progressive collapse resulting from locally induced damages (e.g., sudden removal of a critical member), this paper proposed a generalized framework to identify the critical members and to qualify the alternate load path (ALP) redundancy of steel truss bridges subjected to sudden member loss of a critical member. Demand-to-capacity ratio (DCR) for linear elastic analysis and strain ratio (SR) for nonlinear dynamic analysis were employed as the design metrics and performance indicators to qualify the ALP redundancy of steel truss bridges. Effectiveness of the proposed framework in qualifying the ALP redundancy of steel truss bridges was demonstrated through the I-35W truss bridge under sudden member removal (MR) analyses. Results obtained from this study may provide beneficial information and could serve as good references for the protection of steel truss bridges that are vulnerable to sudden member loss scenarios.

KAJIAN RESPON JEMBATAN DI ACEH UTARA TERHADAP GEMPA BERDASARKAN DATA MIKROTREMOR

The parameters of the bridge's resistance to dynamic loads, such as natural frequency and damping ratio, were determined by theoretical and experimental modal analysis. Microtremor measurements were performed on two types of bridges: steel truss bridges and reinforced concrete bridges. The goal is to establish the value of the bridge's dynamic characteristics as well as the bridge's feasibility based on SNI 2833-2008's natural vibration period limit. The natural frequency of the dynamic test is used to compare natural frequencies to theoretical frequencies to determine the condition of the integrity and level of stiffness of the structure or structural elements. The HVSR (Horizontal to Vertical Spectral Ratio) approach is used to analyze microtremor data on the ground, while FSR (Floor Spectral Ratio) and RDM (Random Decrement Method) analysis is used to analyze microtremor data on bridges. According to the findings of the first case study, the natural frequency of the soil is 1.25–1.35 Hz. The steel truss bridge's natural frequency value from the FSR is 0.81–3.29 Hz, while the RDM values are 1.12–3.46 Hz, indicating that the frequency produced is less stable on the NS component. The damping ratio is 2.02–4.97% in this case. The same analysis was carried out on a different type of bridge in the second case study, with a natural ground frequency of 1.11 - 1.47 Hz. The reinforced concrete bridge's natural frequency value from the FSR is 2.21 - 3.62 Hz, and the RDM results are 2.30 - 3.62 Hz. In each component, the frequency found tends to be stable. The damping ratio result is 2.05 - 4.81%. The period of the experimental results shows identical results to the period of the theoretical calculation results, with the average experimental result period being 7.24% greater than the period of the theoretical calculation results. According to the experimental data, the bridge damping ratio is around 3.16% . This result is in accordance with the literature, which is around 2-5%.

Structural damage detection of old ADA steel truss bridge using vibration data

This study proposes a statistical-based detection method that is responsive to damage and not to environmental and operational conditions. The method serves as a damage recognition system for structural health monitoring using field measurements from real bridges. Vehicle-induced bridge and ambient vibration measurements collected from the benchmark Old ADA steel truss bridge situated in Japan were utilized to validate the proposed method. The steel truss members in the bridge were subjected to five different damage scenarios to represent common potential problems in structural health monitoring of real-life applications. The collected measurements have been completely published and made available online. A combination of principal component analysis (PCA) and linear discriminant analysis (LDA) transformation is utilized as a statistical-based recognition technique. Vibration data representing power spectral density (PSD) functions were tested as damage-sensitive features from identified condition sources. The proposed combination of the PCA-LDA transformation system outperforms the popular PCA transformation as a statistical model for classification of state conditions. Although the first two principal components of PCA hold 50–85% of the variation in data, the first two components from PCA- LDA hold about 95% of the total variation. As a result, the three PCs, of PCA-LDA, visualization successfully managed to classify the five structural damage scenarios into their five individual subgroups.

Damage Detection of Steel Truss Bridges Based on Gaussian Bayesian Networks

This paper proposes the use of Gaussian Bayesian networks (GBNs) for damage detection of steel truss bridges by using the strain monitoring data. Based on the proposed damage detection procedure, a three-layer GBN model is first constructed based on the load factors, structural deflections, and the stress measurements of steel truss bridges. More specifically, the load factors of the structures are defined as the first-layer network nodes, structural deflections are considered as the second-layer network nodes, and the third-layer nodes of the GBN model are built based on the stress data of the truss elements. To achieve the training for the constructed GBN model, the finite element analysis of the bridge structures under the different load factors is performed. Then, the training of the network is performing by using the maximum likelihood estimation approach, and the optimized network parameters are obtained. Based on the trained network model, the measured load factors and the corresponding stress monitoring data of a limited number of truss elements are considered as input, and the stress measurements of all truss elements of bridges can be accurately estimated by searching the optimized topological information among network nodes. For a steel truss bridge, when the truss elements are damaged, the stress states of the damaged elements will be changed. Therefore, a damage index is further constructed for damage detection of steel truss bridges based on the changed stress states of those damaged elements. To verify the feasible and effective use of the proposed damage detection approach, an 80 m steel truss bridge with various damage cases was conducted as numerical simulations, and the investigation results show that the trained GBN can be accurately used for stress prediction of steel truss bridges, and the proposed damage index with the estimated stress data can be further applied for structural damage localization and quantification with a better accuracy. Furthermore, the results also suggest that the proposed damage detection procedure is accurate and reliable for steel truss bridges under vehicle loads.

Acceleration Based Condition Monitoring of Steel Bridges

This paper presents a frequency domain-based method for continuous health monitoring of steel truss bridge, based on the power spectral density and frequency analysis of the recorded acceleration response time histories at the selected node points of a bridge under regular operational conditions. This method is also implemented on a railway steel truss bridge connecting the mainland India to Rameswaram, named the Pamban bridge. The measured acceleration response time histories at some selected nodes on the bridge were parameterized into 4 power spectral density and frequency-based parameters. Also, the frequency analysis of the acceleration response time histories was done. The continuous variation of these parameters with measurement points was observed for about a year to investigate the suitability of these parameters to detect, locate damages in a railway steel truss bridge, and find their severity. Then, the consistency of damages detected by the variation of these parameters and the damages expected based on strain measurements and visual inspections made on the bridge was examined to evaluate the suitability of these parameters for the level 3 damage detection. The same is documented and performance of the parameters discussed.

PERFORMANCE COMPARISON OF STRUCTURAL DAMAGE DETECTION METHODS BASED ON FREQUENCY RESPONSE FUNCTION AND POWER SPECTRAL DENSITY

Recent catastrophic events have aroused great interest in the scientific community regarding the evaluation and prediction of the structural response along the life cycle of infrastructures. Efforts are put into developing adequate health monitoring systems to help prevent future human life and economic losses. Here, two non-destructive damage detection methods are presented: the Frequency Response Function-based and the Spectral Density Function-based methods. The damage detection performance of both methods is compared through a particular case study, where different damage scenarios are analyzed in a 2D truss bridge. The reliability of each method is studied in terms of different prediction errors. Numerical results show that the PSD method for damage detection on a steel truss bridge structure provides more accurate and robust results when compared to that based on FRF. Keywords: Structural Health Monitoring, Power Spectral Density Function, Frequency Response Function, Construction, Structures, Damage detection, Non-destructive

Detection of Nut–Bolt Loss in Steel Bridges Using Deep Learning Techniques

The early detection of bolts and nuts’ loss on bridges has a huge tendency of averting bridge collapse. The aim of this research is to develop a novel framework for the detection of bolt–nut losses in steel bridges using deep learning techniques. The objectives include: to design a framework for the detection of nuts and bolts and nut holes using deep learning techniques, to implement the designed framework using Python programming, and to evaluate the performance of the designed framework. Convolutional neural network (CNN) and long- and short-term memory (LSTM) techniques were employed using 8 × 8 blocks of images of bridges as inputs. Based on the proposed models, which considered the CNN in its ordinary form, and combined with the LSTM and You Only Look Once (YOLOv4) algorithms, the CNN achieved average classification accuracy of 95.60% and the LSTM achieved an accuracy of 93.00% on the sampled images. The YOLOv4 algorithm, which is a modified version of the CNN with single forward propagation, was utilized, and the detection accuracy was 76.5%. The relatively high level of detection accuracy recorded by the CNN is attributed to its stepwise extraction by convolution and pooling processes. However, a statistical test of the hypothesis at the 5.0% level of significance revealed that there was no statistically significant difference between object detection and classifications among the models used in the built framework. Therefore, the use of the CNN model is recommended for the detection of nuts and bolts and nut holes on steel truss bridges for effective structural health monitoring (SHM) purposes based on its high level of detection accuracy and speed.

Research Status and Development Prospect of Steel Truss Bridge Joints

In recent decades, with the increasing demand of long-span bridges and the continuous progress of construction technology, steel truss bridges have been more and more widely used. In general, the joint form of steel truss bridge can be divided into two types: splice joint and integral joint. The selections of the joint forms in steel truss bridges have gradually become the focus of the designers and researchers. Based on the engineering cases of steel truss bridges, this paper introduces the characteristics and advantages of the splice joint and the integral joint, respectively. The engineering performances of these two kinds of joints are compared under the same working condition. The corresponding conclusions on joint selection are obtained, and the prospect of joint design for steel truss bridges is proposed.

ALTERNATIF PERENCANAAN SUPER STRUCTURE JEMBATAN RANGKA BAJA PADA JEMBATAN WAY UMALOYA DI KABUPATEN KEPULAUAN SULA

The Way Umaloya Bridge in the Sula Islands Regency is a reinforced concrete girder type bridge which has a total bridge span length of 60.00 with field survey results there are cracks in each of the longitudinal and transverse girders, corrosion of steel reinforcement and deflection due to repeated loads. The purpose of this research is as a planning alternative to analyze the dimensions of each element of the superstructure of the steel frame bridge in the redesign. In planning the superstructure of the steel truss bridge, warren truss type is used with analysis and design calculations using the LRFD method for control of the steel frame, such as tensile bars, compression rods, bending rods and bolted connections. In each calculation, the superstructure elements are controlled against the requirements for selecting the profile dimensions and controlled for materials and stresses. In planning the super structure of the bridge, the results of the analysis and design were obtained as follows: the use of dimensions and reinforcement on the pavement floor plate and vehicle floor plate used the dimensions of the pavement floor plate 25 x 50 x 6000 cm, with the main reinforcement requirements ø 16-12 mm and reinforcement for ø 12-200 mm, vehicle floor plate dimensions are used 20 x 600 x 6000 cm, with the need for basic reinforcement ø 16-100 mm, reinforcement for ø 12-200, the use of longitudinal girders with dimensions of WF profile 450 x 200 x 9 x 14 mm , the use of transverse girders with a WF profile dimension of 800 x 300 x 14 x 26 mm, the use of a main frame with a WF profile dimension of 400 x 400 x 45 x 70 mm, the use of wind ties with a WF profile dimension of 150 x 150 x 7 x 10 mm, the use of connection on the node plate with a plate thickness of 2.5 cm, the use of connecting plate connections between longitudinal girders, transverse girders, and the main frame with a profile dimension of L 100.100.10 mm. and the use of connection plates on wind ties with a dimension of 1.0 cm thick C profile.

Increasing Inventory Rating Factor of Steel Truss Bridge Through Orthotropic Steel Deck Panel Application

Currently, 18,648 bridges with a total length of 510,366 km have been constructed in Indonesia, but only 86% are in good condition, while the rest are damaged. Steel truss bridge damage generally occurs on the RC decks, and its repair is often implemented through deck replacement or redecking using Orthotropic Steel Deck (OSD) panel. In Indonesia, this method has only been applied limitedly at the Citarum I Bridge in 2009 and the Cisadane Bridge in 2013, while the effect on the existing steel truss bridge is unknown. Therefore, this study aims to evaluate the steel truss bridge performance after OSD panel redecking through numerical modeling. The design process of the OSD panel was carried out by micro-modeling on ABAQUS CAE using shell elements with a mesh size of 50x50 mm and pinned boundary conditions. In this stage, the materials were assumed to be elastic with small deformations. The evaluation of steel truss bridge performance was performed on the A-class steel truss bridge Bina Marga design standard with a 60 m span by comparing the existing bridge inventory rating factor (using RC decks) to OSD panel redecking, which is an indicator of bridge self-weight reduction. Based on the structural macro-model developed using SAP2000, the bridge self-weight reduced the axial tension and compression forces on the steel truss bridge mainframe by 20.6%-24.6% and 20.5%-24.5%, respectively. Consequently, this increased the inventory rating factor by 9.3%-9.5%. In other words, using the OSD panels lighter than the existing RC decks increases the steel truss bridge capacity to resist the live load or vehicle rating throughout its service life.

APPLICATION OF BUCKLING RESTRAINED DAMPER TO TRUSS BRIDGE FOR BETTER SEISMIC PERFORMANCE

The buckling restrained bracing (BRB) is widely used to improve seismic behavior of buildings. It is employed for bridges as well, but the application is very limited. The BRB can be used also as a damper, in which case the device may be called a buckling restrained damper (BRD). Yet such application has not been explored. There are quite a few bridges designed by the old design codes in Japan. Their seismic resistance may not be satisfactory for the current seismic design codes. Against this background, the behavior of a steel truss bridge under a large seismic loading is investigated by the nonlinear dynamic finite element analysis. Some members are indeed found damaged in the earthquake. Retrofit is needed. To this end, the application of the BRD is tried in the present study: a parametric study on the seismic behavior of a steel truss bridge with BRD is conducted by changing the length and the cross-sectional area of the yielding core of the BRD. The study concludes that with the application of well-designed BRDs to appropriate locations, a steel truss bridge could go through a large earthquake without damage.

Vibration-based Damage Localization and Quantification Framework of Large-Scale Truss Structures

The use of structural health monitoring (SHM) systems on a regular basis is critical to achieve early damage detection, avoid unpredicted failures, and perform cost-effective maintenance planning. The main objective of this work is to present a model-based Damage Detection Framework for truss structural systems that uses output-only vibration measurements. Model-based methods provide much more comprehensive information about the condition of the monitored system than the data-driven and also allow the prediction of the location and level of damage. The measured vibration response of a healthy structural system under operational vibrations is employed to tune a parameterized FE model using state-of-the-art FE model updating techniques to obtain an optimal numerical model of the structural system. Based on the optimal FE model, a set of damaged FE models is generated for selected damage scenarios. A damage approximation approach that represents local damage with uniform stiffness reduction is also presented. In the Damage Detection Framework, the vibration data records for both the “healthy” and the “damaged” structure and the results from multiple numerical analysis on the “healthy” and the “damaged” FE models are used. The transmittance functions for the “healthy” and “damaged” states of the structure and the FE models are derived to calculate the damage indicators. Using these indicators, potentially damaged structural members are identified, grouped, and compared to finally locate the specific damaged member. The proposed framework provides both accurate damage localization and damage quantification using a limited number of sensors for unknown input excitation. Herein, the case study used is a laboratory steel truss bridge.

Data-driven structural condition assessment for high-speed railway bridges using multi-band FIR filtering and clustering

The monitoring data makes it feasible to inspect and assess the structural condition of bridges in real time. Among the diverse in situ data of high-speed railway bridges under varying operational environments, dynamic responses caused by passing trains can offer insight into the mechanical properties of the bridge structure. Based on the train-induced response features of influence line (IL) and dynamic load factors (DLF) extracted from raw measured data, a comprehensive data-driven approach is developed for structural condition assessment of high-speed railway bridges, which is applied to a long-span steel-truss bridge as a validation. Considering the sparsity of the train-induced static response in the frequency domain, the multi-band finite impulse response (MFIR) filtering is used to extract the train-induced response features. The features are clustered via the Gaussian mixture model (GMM), and the two-level objective for structural condition evaluation and degradation warning of bridges can be achieved through the dynamically updated clustering results and probabilistic models. The results demonstrate that (1) MFIR filtering can effectively reject abnormal or interfering data and accurately extract the train-induced response features, and (2) the intrinsic nature and laws of features can be revealed by GMM clustering, which provides a statistical premise for the reliability analysis of bridge structures.

APPLICATION OF BUCKLING RESTRAINED DAMPER TO TRUSS BRIDGE FOR BETTER SEISMIC PERFORMANCE

The buckling restrained bracing (BRB) is widely used to improve seismic behavior of buildings. It is employed for bridges as well, but the application is very limited. The BRB can be used also as a damper, in which case the device may be called a buckling restrained damper (BRD). Yet such application has not been explored. There are quite a few bridges designed by the old design codes in Japan. Their seismic resistance may not be satisfactory for the current seismic design codes. Against this background, the behavior of a steel truss bridge under a large seismic loading is investigated by the nonlinear dynamic finite element analysis. Some members are indeed found damaged in the earthquake. Retrofit is needed. To this end, the application of the BRD is tried in the present study: a parametric study on the seismic behavior of a steel truss bridge with BRD is conducted by changing the length and the cross-sectional area of the yielding core of the BRD. The study concludes that with the application of well-designed BRDs to appropriate locations, a steel truss bridge could go through a large earthquake without damage.

Review and discussion on fire behavior of bridge girders

This paper presents an overview on fire behavior of bridge girders mainly including prestressed concrete (PC) bridge girders and steel bridge girders. The typical fire accidents occurred on bridges are illustrated and, the seriousness of posing threats to bridge structures resulted from increasing traffic fires, specially intense hydrocarbon fires generated from petrol-chemicals, is highlighted. The current researches, embracing high-temperature properties of constituent materials, prestress state, measurement in fire tests, numerical methods, structural fire resistance, and so forth, taken on coping with problems existing in fire behavior and structural fire behavior in bridge girders are reviewed and discussed. Further, strategies for enhancing fire resistance of bridge girders followed with failure criterion and mode in types of bridge structures are provided. Future research area along with emerging trends in structural fire behavior of bridge girders is also recommended for mitigating fire hazards occurred on bridge girders. Herein, it can be attained a conclusion from review and discussion that prestressed concrete bridge girders with thin webs, specially T-shaped bridge girder, are prone to unstable under fire exposure conditions. High-strength concrete utilized in prestressed concrete bridge girders is vulnerable to spalling at elevated temperature. Steel-truss bridge girder present a more significant fragility to fire exposure compared than other steel bridge girders. • Fire behavior of bridge girders is reviewed and discussed. • Seriousness of bridge fires is highlighted. • Strategies for enhancing fire resistance of bridge girders are provided. • Fire resistance in types of bridge girders is investigated and presented. • Steel-truss bridge girders are significantly vulnerable to fire damage.

Mapping of Temperature-Induced Response Increments for Monitoring Long-Span Steel Truss Arch Bridges Based on Machine Learning

Temperature-induced responses have been found to be sensitive to changes in bridge properties. Accordingly, researchers have sought to develop temperature-response mappings that could be used in assessing bridge conditions; to date, obtaining sufficiently precise mappings analytically has proven intractable. Alternatively, numerous researchers have directly developed mappings between temperature and the associated responses using measured data. However, temperature-induced responses are a function of the temperatures throughout the entire bridge, and such spatial temperature distributions using a limited number of sensors are challenging to capture, particularly for steel truss bridges, due to the large number and variety of structural members. Mappings that have been obtained are generally a function of the long-term fluctuations, corresponding to daily variations; the short-term fluctuations (i.e., higher-frequency components) in temperature data are neglected. This paper first proposes that the relationship between increments in temperature and the associated increments in responses can be used as a surrogate to assess the bridge performance. Simulation results show that the statistical distribution of the error between measured and predicted response increments can be used for identifying abnormal structural behavior. Then, various mappings for both displacement and strain increments are explored and verified using field monitoring data. The mapping with all temperature sensors performs the best; principal component analysis (PCA) can effectively reduce the dimension of input without compromising accuracy. In addition, the recorded time of temperature data is validated to be a useful indicator of the spatial temperature distribution in bridges, which can be used to improve the performance of the mappings when the bridge has only a few temperature sensors. These findings provide an improved approach for mapping the relationship between increments in temperature to increments in temperature-induced responses that shows promise for identifying abnormal bridge behavior.