scholarly journals Simultaneous Identification of Bridge Structural Damage and Moving Loads Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies

2021 ◽  
Vol 14 (1) ◽  
pp. 119
Author(s):  
Solmaz Pourzeynali ◽  
Xinqun Zhu ◽  
Ali Ghari Zadeh ◽  
Maria Rashidi ◽  
Bijan Samali
Keyword(s):  
Explicit Form ◽  
Structural Damage ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Condition Assessment ◽  
Vehicle Speed ◽  
Moving Loads ◽  
Road Roughness ◽  
Bridge Structures ◽  
Simultaneous Identification

Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for condition assessment of the bridge structures. Here, the simultaneous identification of moving loads and structural damage based on the explicit form of the Newmark-β method is proposed. Although there is an extensive attempt to identify moving loads with known structural parameters, or vice versa, their simultaneous identification considering the road roughness has not been studied enough. Furthermore, most of the existing time domain methods are developed for structures under non-moving loads and are commonly formulated by state-space method, thus suffering from the errors of discretization and sampling ratio. This research is believed to be among the few studies on condition assessment of bridge structures under moving vehicles considering factors such as sensor placement, sampling frequency, damage type, measurement noise, vehicle speed, and road surface roughness with numerical and experimental verifications. Results indicate that the method is able to detect damage with at least three sensors, and is not sensitive to sensors location, vehicle speed and road roughness level. Current limitations of the study as well as prospective research developments are discussed in the conclusion.

scholarly journals Comprehensive Study of Moving Load Identification on Bridge Structures Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies

2021 ◽  
Vol 13 (12) ◽  
pp. 2291
Author(s):  
Solmaz Pourzeynali ◽  
Xinqun Zhu ◽  
Ali Ghari Zadeh ◽  
Maria Rashidi ◽  
Bijan Samali
Keyword(s):  
Explicit Form ◽  
Numerical Study ◽  
Moving Load ◽  
Experimental Studies ◽  
Sampling Frequency ◽  
Vehicle Speed ◽  
Load Identification ◽  
Simply Supported ◽  
Bridge Structures ◽  
Moving Load Identification

Bridge infrastructures are continuously subject to degradation due to aging and excess loading, placing users at risk. It has now become a major concern worldwide, where the majority of bridge infrastructures are approaching their design life. This compels the engineering community to develop robust methods for continuous monitoring of bridge infrastructures including the loads passing over them. Here, a moving load identification method based on the explicit form of Newmark-β method and Generalized Tikhonov Regularization is proposed. Most of the existing studies are based on the state space method, suffering from the errors of a large discretization and a low sampling frequency. The accuracy of the proposed method is investigated numerically and experimentally. The numerical study includes a single simply supported bridge and a three-span continuous bridge, and the experimental study includes a single-span simply supported bridge installed by sensors. The effects of factors such as the number of sensors, sensor locations, road roughness, measurement noise, sampling frequency and vehicle speed are investigated. Results indicate that the method is not sensitive to sensor placement and sampling frequencies. Furthermore, it is able to identify moving loads without disruptions when passing through supports of a continuous bridge, where most the existing methods fail.

scholarly journals A new approach to assess the influence of road roughness on driver speed behavior based on driving simulator tests

2016 ◽  
Vol 11 (2) ◽  
pp. 144-152 ◽  
Author(s):  
Mariano Pernetti ◽  
Mauro D’Apuzzo Mauro D’Apuzzo ◽  
Francesco Galante
Keyword(s):  
Driving Simulator ◽  
Experimental Studies ◽  
Vertical Vibration ◽  
The Other ◽  
Added Value ◽  
Published Data ◽  
Vehicle Speed ◽  
Safety Level ◽  
The Road ◽  
Road Roughness

Vehicle speed is one of main parameters describing driver behavior and it is of paramount importance as it affects the travel safety level. Speed is, in turn, affected by several factors among which in-vehicle vibration may play a significant role. Most of speed reducing traffic calming countermeasures adopted nowadays rely on vertical vibration level perceived by drivers that is based on the dynamic interaction between the vehicle and the road roughness. On the other hand, this latter has to be carefully monitored and controlled as it is a key parameter in pavement managements systems since it influences riding comfort, pavement damage and Vehicle Operating Costs. There is therefore the need to analyse the trade-off between safety requirements and maintenance issues related to road roughness level. In this connection, experimental studies aimed at evaluating the potential of using road roughness in mitigating drivers’ speed in a controlled environment may provide added value in dealing with this issue. In this paper a new research methodology making use of a dynamic driver simulator operating at the TEST Laboratory in Naples is presented in order to investigate the relationship between the driver speed behavior on one hand, and the road roughness level, road alignment and environment, vehicle characteristics on the other. Following an initial calibration phase, preliminary results seem fairly promising since they comply with the published data derived from scientific literature.

A Multi-State Strategy for Structural Damage Detection Using Sensitivity of Weighted Transmissibility Function

2021 ◽  
pp. 2150144
Author(s):  
Ziwei Luo ◽  
Huanlin Liu ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Fe Model ◽  
Limited Information ◽  
Structural Damage Detection ◽  
State Strategy ◽  
Output Only

In practice, a model-based structural damage detection (SDD) method is helpful for locating and quantifying damages with the aid of reasonable finite element (FE) model. However, only limited information in single or two structural states is often used for model updating in existing studies, which is not reasonable enough to represent real structures. Meanwhile, as an output-only damage indicator, transmissibility function (TF) is proven to be effective for SDD, but it is not sensitive enough to change in structural parameters. Therefore, a multi-state strategy based on weighted TF (WTF) is proposed to improve sensitivity of TF to change in parameters and in order to further obtain a more reasonable FE model for SDD in this study. First, WTF is defined by TF weighted with element stiffness matrix, and relationships between WTFs and change in structural parameters are established based on sensitivity analysis. Then, a multi-state strategy is proposed to obtain multiple structural states, which is used to reasonably update the FE model and detect structural damages. Meanwhile, due to fabrication errors, a two-stage scheme is adopted to reduce the global and local discrepancy between the real structure and the FE model. Further, the [Formula: see text]-norm and the [Formula: see text]-norm regularization techniques are, respectively, introduced for both model updating and SDD problems by considering the characteristics of problems. Finally, the effectiveness of the proposed method is verified by a simply supported beam in numerical simulations and a six-storey frame in laboratory. From the simulation results, it can be seen that the sensitivity to structural damages can be improved by the definition of WTF. For the experimental studies, compared with the FE model updated from the single structural state, the FE model obtained by the multi-state strategy has an ability to more reasonably describe the change of states in the frame. Moreover, for the given structural damages, the proposed method can detect damage locations and degrees accurately, which shows the validity of the proposed method and the reliability of the updated FE model.

Structural Damage Detection Using Auto/Cross-Correlation Functions Under Multiple Unknown Excitations

2014 ◽  
Vol 14 (05) ◽  
pp. 1440006 ◽  
Author(s):  
Pinghe Ni ◽  
Yong Xia ◽  
Siu-Seong Law ◽  
Songye Zhu
Keyword(s):  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Detection Methods ◽  
Structural System ◽  
Vibration Responses ◽  
Structural System Identification

Traditional structural system identification and damage detection methods use vibration responses under single excitation. This paper presents an auto/cross-correlation function-based method using acceleration responses under multiple ambient white noise or impact excitations. The auto/cross-correlation functions are divided into two parts. One is associated with the structural parameters and the other with the energy of the excitation. These two parts are updated sequentially using a two-stage method. Numerical and experimental studies are conducted to demonstrate the accuracy and robustness of the proposed method. The effects of measurement noise and number of measurement points on the identification results are also studied.

The York-Arup cable experiments: Implications for the fire design of cable-supported bridges

2021 ◽  
Author(s):  
Benjamin Nicoletta ◽  
John Gales ◽  
Panagiotis Kotsovinos
Keyword(s):  
Thermal Strain ◽  
Experimental Studies ◽  
Thermal Response ◽  
Structural Response ◽  
Fire Performance ◽  
Stay Cable ◽  
Stay Cables ◽  
Bridge Structures ◽  
Recent Trends ◽  
High Level

<p>Recent trends towards performance-based fire designs for complex and critical structures have posed questions about the fire resilience of bridge infrastructure. There are little-to-no code requirements for bridge fire resistance and practitioner guidance on the subject is limited. Research on the fire performance of cable-supported bridge structures is scarce and knowledge gaps persist that inhibit more informed fire protection designs in a variety of bridge types. There have been few numerical or experimental studies that investigate the fire performance of steel stay-cables for use in cable-supported bridges. The thermal response of these members is critical as cable systems are highly dependent on the response of individual members, such as in the case of an anchor cable for example. The study herein examines the thermal response of several varieties of unloaded steel- stay cable during exposure to a non-standard methanol pool fire and the implications for the structural response of a cable-supported bridge. Experimental thermal strain data from fire tests of various stay-cables is used to inform high-level insights for the global response of a cable-supported bridge. Namely, the effects of cable thermal expansion on the overall cable system is approximated.</p>

scholarly journals A Novel Piezoceramic-Based Sensing Technology Combined With Visual Domain Networks for Timber Damage Quantification

2021 ◽  
Vol 8 ◽  
Author(s):  
Haibei Xiong ◽  
Lin Chen ◽  
Cheng Yuan ◽  
Qingzhao Kong
Keyword(s):  
Time Domain ◽  
Damage Assessment ◽  
Structural Damage ◽  
High Efficiency ◽  
Experimental Studies ◽  
Stress Wave Propagation ◽  
Timber Structures ◽  
Sensing Technology ◽  
The Time Domain ◽  
Visual Domain

Early detection of timber damage is essential for the safety of timber structures. In recent decades, wave-based approaches have shown great potential for structural damage assessment. Current damage assessment accuracy based on sensing signals in the time domain is highly affected by the varied boundary conditions and environmental factors in practical applications. In this research, a novel piezoceramic-based sensing technology combined with a visual domain network was developed to quantitatively evaluate timber damage conditions. Numerical and experimental studies reveal the stress wave propagation properties in different cases of timber crack depths. Through the spectrogram visualization process, all sensing signals in the time domain were transferred to images which contain both time and frequency features of signals collected from different crack conditions. A deep neural network (DNN) was adopted for image training, testing, and classification. The classification results show high efficiency and accuracy for identifying crack conditions for timber structures. The proposed technology can be further integrated with a fielding sensing system to provide real-time monitoring of timber damage in field applications.

Predicting Truck Load Variation Using Q-Truck Model

2014 ◽  
Vol 534 ◽  
pp. 105-110
Author(s):  
Rosnawati Buhari ◽  
Mohd Ezree Abdullah ◽  
Munzilah Md Rohani
Keyword(s):  
Asphalt Concrete ◽  
Radial Strain ◽  
Suspension System ◽  
Vehicle Speed ◽  
Pavement Distress ◽  
Traffic Loads ◽  
Road Roughness ◽  
Heavy Truck ◽  
Truck Load ◽  
Pavement Damage

The study of heavy vehicle forces on pavement is important for both vehicle and pavement. Indeed it was identified several factors such as environment, materials and design consideration affects pavement damage over time with traffic loads playing a key role in deterioration. Therefore, this paper presents dynamically varying tire pavement interaction load, thus enable to assess the strain response of pavements influenced by road roughness, truck suspension system, variation of axle loading and vehicle speed. A 100m pavement with good evenness was simulated to check the sensitivity of the dynamic loads and heavy truck vertical motions to the roughness. The most important performance indicators that are required in pavement distress evaluation are radial strain at the bottom of the asphalt concrete and vertical strain at the subgrade surface was predicted using peak influence function approach. The results show that truck speed is the most important variables that interact with truck suspension system and thus effect of loading time are extremely important when calculating the critical.

Local Mass Addition and Data Fusion for Structural Damage Identification Using Approximate Models

2020 ◽  
Vol 20 (11) ◽  
pp. 2050124
Author(s):  
Jilin Hou ◽  
Zhenkun Li ◽  
Qingxia Zhang ◽  
Łukasz Jankowski ◽  
Haibin Zhang
Keyword(s):  
Data Fusion ◽  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Parameters ◽  
Identification Accuracy ◽  
Fe Model ◽  
Local Damage ◽  
Approximate Models ◽  
Structural Damage Identification

In practical civil engineering, structural damage identification is difficult to implement due to the shortage of measured modal information and the influence of noise. Furthermore, typical damage identification methods generally rely on a precise Finite Element (FE) model of the monitored structure. Pointwise mass alterations of the structure can effectively improve the quantity and sensitivity of the measured data, while the data fusion methods can adequately utilize various kinds of data and identification results. This paper proposes a damage identification method that requires only approximate FE models and combines the advantages of pointwise mass additions and data fusion. First, an additional mass is placed at different positions throughout the structure to collect the dynamic response and obtain the corresponding modal information. The resulting relation between natural frequencies and the position of the added mass is sensitive to local damage, and it is thus utilized to form a new objective function based on the modal assurance criterion (MAC) and [Formula: see text]-based sparsity promotion. The proposed objective function is mostly insensitive to global structural parameters, but remains sensitive to local damage. Several approximate FE models are then established and separately used to identify the damage of the structure, and then the Dempster–Shafer method of data fusion is applied to fuse the results from all the approximate models. Finally, fractional data fusion is proposed to combine the results according to the parametric probability distribution of the approximate FE models, which allows the natural weight of each approximate model to be determined for the fusion process. Such an approach circumvents the need for a precise FE model, which is usually not easy to obtain in real application, and thus enhances the practical applicability of the proposed method, while maintaining the damage identification accuracy. The proposed approach is verified numerically and experimentally. Numerical simulations of a simply supported beam and a long-span bridge confirm that it can be used for damage identification, including a single damage and multiple damages, with a high accuracy. Finally, an experiment of a cantilever beam is successfully performed.

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