Journal of Infrastructure Intelligence and Resilience最新文献

Looseness detection in bolt-connected joints is vital in ensuring safety and keeping the service stability of structures. Thus, various structural health monitoring methods have been introduced for bolt looseness detection by many researchers. However, most of them studied a single bolt, which may not be readily applicable to actual structures. In this study, a SH-type guided wave-based method is presented for bolt looseness detection and localization of a joint with multiple bolts using a small number of magnetostrictive transducers. A normalized wave energy transmission ratio $I_{BL}^{nor}$ was used as a bolt looseness index, which was defined on the basis of the wave energy ratios between the transmitted wave passing through the joint and the directly incoming wave from the actuator. Several wave propagation paths in the pitch-catch tests were considered, and the $I_{BL}^{nor}$ values from the wave paths were used as the input to the backpropagation neural network (BPNN) for bolt looseness localization and severity estimation. Numerical and experimental studies were conducted on a lap joint with eight bolts. The results show that the bolt looseness conditions can be successfully estimated for the experimental data using the BPNN trained by the $I_{BL}^{nor}$ generated from the finite element simulation. Noise-injected learning was conducted in the training process to improve the bolt looseness localization accuracy.

The demands for infrastructure inspection using autonomous robots have noticeably increased, and the market is expected to grow accordingly. One of the advantages is that autonomous robots can navigate the environment and interact with humans because an inspection of a high-rise building, for instance, is considered an extremely challenging task for a human. Inspection robot systems can be classified as ground, aerial, underwater robots, or types of sensors used for inspection, such as visual or non-visual sensors. Users can choose a specific robot platform for their target and environment among them. This paper reviews various inspection robots and categorizes them according to their automated inspection system to aid the user in a good choice. Especially, unmanned aerial vehicles (UAVs) are preferred among the various robot platforms due to their high manoeuvrability. Thus, two types of aerial inspection robot platforms, such as climbing aerial robots and autonomous drone navigation systems, are introduced in detail.

Civil infrastructure worldwide is ageing and enduring increasingly adverse weather conditions. Traditional structural health monitoring (SHM) involves the expensive and time-consuming installation of contact sensors. For example, inspectors use costly large-scale equipment to reach a certain area of the structure and at different heights to inspect it, which can pose a risk to the inspector's safety. Moreover, the inspectors rely only on the batch data acquired during the inspection period, which are analyzed by engineers at a later time due to the limited availability of a real-time visualization approach for structural inspection within the traditional mode of SHM. To address these timely challenges, an Augmented Reality (AR)-based automated multiclass damage identification and quantification methodology is proposed in this paper. The interactive visualization framework of AR is integrated with the autonomous decision-making of Artificial Intelligence (AI) in a unified fashion to incorporate human-sensor interaction. The proposed system uses an AI model that is trained and optimized using the YOLOv5 architecture to detect and classify four different types of anomalies/damages (i.e., cracks, spalls, pittings, and joints). The AI model is then updated to quantify the length, area, and perimeter of any damage using segmentation to further assess its severity. Once the model is developed, the model is embedded with the AR device and tested through its interactive environment for SHM of various structures. The paper concludes that the proposed approach successfully classifies four types of damage with an accuracy of more than 90% for up to 2 ​m, and it also quantifies the length, area, and perimeter with less than 2% of error.

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.