Journal of Infrastructure Intelligence and Resilience最新文献

Transversely installing the dampers on the stay cable has been widely adopted to control its excessive vibration. However, the optimum damper size and its damping efficiency is subject to the effect of damper parameters, including the damper coefficient, damper inner stiffness and support stiffness, damper concentrated mass. Based on the attainable damping-ratio formulas of the stay cable–damper system proposed by authors, this study carries out a serials of experimental study on the cable-damper system to investigate the effect of the above-mentioned damper parameters and to consolidate the accuracy of the proposed damping-ratio equation. A scaled sagged stay cable has been built, and a small-size shear-mode viscoelastic damper has been developed. Results indicate that the larger damper stiffness and the lower support stiffness degrade the achievable damping ratio. Increasing the damper mass properly seems to improve the achievable damping ratio but still needs more full-scale test verification. The sag effect of the cable reduces considerably the attainable damping ratio for the first-order mode while affect marginally for the higher mode. Experimental results of the attainable damping-ratio considering the effect of the damper parameters commonly align with the theoretical values from the design formula. Therefore, the design formula is qualified to facilitate the design of the damper size.

It is important to evaluate the connectivity reliability of highway networks for the emergency response and rehabilitation of transportation systems in high-intensity seismic regions. Given the complexity and uncertainty of seismic damages of highway networks in high-intensity seismic region, this paper describes a Bayesian network (BN) model for evaluating the network connectivity reliability and identifying critical units. The empirical prediction method is employed to compute the connectivity probability of highway units based on the structural damage of units under earthquakes. A success tree is used to construct the network connectivity graph. Then, the network connectivity graph is converted into the BN model by BN method with the connectivity probability of highway units as prior probability. Sensitivity analysis and Bayesian updating are performed in BN to identify critical units and dynamically assess the connectivity reliability of highway network. The proposed model is applied to a highway network composed of G213 and S9 in the Wenchuan Earthquake. The results show that the BN model integrates the structural damage of units with the functional performance of the highway network in high-intensity seismic region. Bayesian updating allows the posterior probability of segments and origin–destination pairs to be computed, providing an online evaluation of the functional performance of the highway network. The identification of critical units at each stage enables seismic reinforcement priority, thus contributing to the rehabilitation on connectivity reliability of network system.

An updated asset inventory enables public transit agencies to make informed decisions on the maintenance and improvement of their physical assets. Conventional asset inventory surveys mainly rely on manual site visits and subsequent analysis, which are time consuming and expensive. Many research projects developed methods to automate condition assessment of civil infrastructure assets, such as road surfaces, structures, and sewage systems; however, research on the automated detection and condition assessment of public transit infrastructure is very limited. This research aims to contribute to addressing this gap by introducing an automated computer vision-based system to detect main assets in transit bus stops and update asset inventories using video frames captured by on-board cameras on operating buses. This system uses existing hardware systems on public buses to gather required data and then uses Deep Convolutional Neural Networks (DCNNs) to recognize public transit assets. In addition, a related method was proposed to process manually collected images for semi-automated asset inventory updating. The experimental results showed more than 95% detection rates in videos, which demonstrate potentials for practical applications.

In structural health monitoring, measuring the accurate and spatially dense response near critical locations of the structure can be advantageous to estimate damage to the structure. Due to several physical restrictions or limitations of the sensing method, it may not always be possible to generate reliable data at critical locations. In this study, a hybrid method is presented that makes use of the measured displacement data and finite element (FE) model of the structure to predict dense full-field response. The presented method can incorporate unknown boundary conditions and unknown body forces by applying correction/fictitious forces to match predicted and measured responses. Using static condensation followed by inverse analysis, these additional forces are found by setting up a least square problem. Due to the problem being ill-posed, L2-penalty is used to control the prediction error. Numerical simulation of a plate subjected to body force showed an accurate prediction of full-field response except for a few boundary locations. To handle this, the proposed method is used in conjunction with linear interpolation near boundary locations. The method is validated in a laboratory experiment for a plate with a notch having displacement measured using Digital Image Correlation (DIC). On comparing strains calculated using predicted displacements, FEM, and DIC, the predicted strains show better agreement with the FEM than DIC. This affirms that the proposed hybrid technique can be used at critical locations where DIC fails to provide reliable strain data.

The International Roughness Index (IRI) is one of the most critical parameters in the field of pavement performance management. Traditional methods for the measurement of IRI rely on expensive instrumented vehicles and well-trained professionals. The equipment and labor costs of traditional measurement methods limit the timely updates of IRI on the pavements. In this article, a novel imaging-based Deep Neural Network (DNN) model, which can use pavement photos to directly identify the IRI values, is proposed. This model proved that it is possible to use 2-dimensional (2D) images to identify the IRI other than the typically used vertical accelerations or 3-dimensional (3D) images. Due to the fast growth in photography equipment, small and convenient sports action cameras such as the GoPro Hero series are able to capture smooth videos at a high framerate with built-in electronic image stabilization systems. These significant improvements make it not only more convenient to collect high-quality 2D images, but also easier to process them than vibrations or accelerations. In the proposed method, 15% of the imaging data were randomly selected for testing and had never been touched during the training steps. The testing results showed an averaged coefficient of determination (R square) of 0.6728 and an averaged root mean square error (RMSE) of 0.50.

The COVID pandemic emphasized the prominent role of accessibility to educational infrastructures in societal performance. This identifies educational buildings as a main target of resilience studies. In turn, the word “sustainability” summarizes a societal need gathering momentum in the last few years. It is becoming the focus of several governmental programs.

The two concepts, resilience and sustainability, are first introduced, as a result of their evolution, in a technical context. In this framework, the study narrows its purposes to cover educational buildings. The goal is to emphasize, among the several aspects one has to consider, those that predominate in the design of educational infrastructures.

This study aims to quantify Rayleigh scattering based measurement accuracy of distributed fiber optic sensors under a heating-holding load protocol and characterize the effect of polymer coating on the sensors as the polymer softens and melts away at elevated temperatures. Two segments of a coated single-mode optical fiber were loosely attached and firmly bonded to a steel plate, respectively. When locally heated up to 405 $°\text{C}$ in a furnace, the two segments were used to measure temperature alone and thermal-induced strain. The axial displacement associated with the strain measurement was compared with that of a dial gauge. A temperature increment of 13 ​°C (≪ 20 ​°C) is recommended to ensure successful correlation analysis of Rayleigh scattering signals. The polymer was found to start softening at 155 $°\text{C}$ and melting at 267 ​°C. As temperature sensors, optical fibers with an insulation sheath can accurately measure temperature untill 155 ​°C without and till 267 ​°C with thermo-mechanical analysis of sheath-fiber bonding behavior. As strain sensors, optical fibers with temperature compensation are accurate for strain measurement up to 155 ​°C, above which strain transfer analysis is required due to softening of the fiber coating. Under a large temperature gradient covering 155–267 °C, the fibers attached on steel plates with epoxy function like an extensometer as a center portion of the coating with the high-end temperature is melted as verified by microscopic analysis.