Journal of Infrastructure Intelligence and Resilience最新文献

Bridge decks tend to degrade faster than other bridge components due to environment exposure and vehicular loading. Periodic degradation monitoring is needed for timely rehabilitation measures and development of service life models in bridge decks. Surface degradation are identified through visual inspection (VI) and post-processing of high-definition imagery. Although VI is the primary NDE method employed by most transportation authorities, many anomalies (e.g., cracking, corrosion, and delamination) remain hidden under the surface until deteriorations have grown large enough to surface (e.g., spalling). Subsurface degradation is best identified through other forms of non-destructive evaluation (NDE). Inferences can be made between the various NDE methods, as the mechanisms behind the damages sensed by each method are shared. For instance, condition map from an NDE method may infer future visible deterioration, as well as condition maps for other NDE methods. In this paper, a deep learning approach based in a conditional generative adversarial network is presented for modeling of plausible visible deterioration and NDE condition maps. Two applications are explored: (i) visualization of plausible future deterioration based on current NDE condition map, and (ii) visualization of condition maps for NDE methods from other NDE methods. Field and experimental data from the BEAST facility at Rutgers University are used to develop the training databases for each application.

The present study investigates the performance of supplemental inerter and spring with on-off effects (ISWOE) in the mitigation of the seismic response of base-isolated structures. Firstly, the response of the rigid base-isolated structure with ISWOE is investigated to see the response control effects of ISWOE under stationary earthquake excitation. The performance of ISWOE in the response reduction of the isolated structure is compared with the corresponding passive inerter and spring. The equivalent damping that was added to the base-isolated structures by the ISWOE was used to measure its performance in mitigating the seismic response. The equivalent damping of the ISWOE is obtained for different values of the isolation period and damping ratio. Subsequently, equations for the equivalent damping of the ISWOE and displacement responses are proposed, and it is observed that they match well with the obtained numerical results. Secondly, using the non-linear model of the ISWOE, the seismic response of flexible base-isolated structures is determined for actual earthquakes, considering different values of the isolation period and ISWOE parameters. The trends of the results of isolated structures with ISWOE under the actual earthquake motions were in good agreement with those under stochastic excitation. Finally, the seismic response of isolated structures with ISWOE by non-linear analysis is compared with the corresponding linear analysis with equivalent parameters of the ISWOE. The isolator displacement of the structures with the ISWOE by the non-linear analysis was observed to match those achieved using the equivalent parameters and by the linear analysis.

Pipelines subject to ground deformations generated by geohazard loads carry high importance on pipeline analysis, design, and assessment due to risk of structural damage or failure. Additionally, internal pressure and temperature variation within an operating pipe induce additional strains in combination with pipe strains generated by ground displacement. In this study, an enhanced predictive method is proposed founded upon methods employed by Zheng et al. (2022) to assess pipeline behaviour subject to permanent ground displacement, while considering effects of internal operating pressure and temperature variation. The finite difference-based method previously proposed for strain analysis of buried steel pipes subject to ground movement ignores the effects of internal pressure and/or temperature loading, limiting the applicability of this approach to exclude the operating conditions of pipelines. To address this limitation, the proposed enhanced method accounts for the initial thermal strains and biaxial stress state in the pipe due to hoop stress generated by internal pressure. These additional strains are considered within the expressions of internal axial force and bending moment, derived based on the actual stress distribution on the pipe cross-section. The accuracy of the proposed method is validated against the finite element method (FEM) with respect to results of strain and deformation demand using several indicative case studies. This research provides an effective method of incorporating temperature and internal pressure loads of pipelines subject to permanent ground displacements of varying types, magnitudes, and directions.

This study assessed the rheological performance of asphalt binder modified with palm oil clinker fine (POCF) and its influence on the asphalt binder's short-term aging properties. The conventional properties of unaged and short-term aged asphalt binders modified with 0%, 2%, 4%, 6%, and 8% POCF content by binder weight were evaluated. The modified binders were then tested using a dynamic shear rheometer (DSR) in a temperature sweep test at temperatures ranging from 25 ​°C to 55 ​°C. To examine the effect of various POCF contents and temperature ranges on the rheological behavior of asphalt binders, the Central Composite Design (CCD) Response Surface Methodology (RSM) design was used with two input response variables, POCF content and temperature, and two rheological parameters as responses, complex modulus, and phase angle. When compared to the virgin binder, POCF-modified binders had higher complex modulus and lower phase angle values at all temperatures. Furthermore, the stiffness of the aged-modified binder was lower at all temperatures than that of the virgin binder. The analysis shows that complex modulus and phase angle have high correlation coefficients (R²) of <0.98, indicating that the model values are strongly correlated with the experimenttal values. The model also demonstrates that temperature has more influence on the rheological performance of the modified binder. Using the generated quadratic model and numerical optimization, optimal values for POCF and temperature were found to be 5.57% and 41.9 ​°C, respectively. The validation test results show that all responses have a percentage error of <5%, indicating good agreement and the model's effectiveness.

More widespread use of industrialized construction (IC) is hampered by the high capital cost of advanced production facilities paired with low profit margins. A novel service-oriented cloud manufacturing (CMfg) model could in theory increase utilization and profitability of distributed production facilities. However, little research has investigated how IC can benefit from the CMfg model. This paper examines opportunities and challenges of applying CMfg for IC. First, an adapted model of CMfg for construction is developed based on a literature review. Second, four possible scenarios for applying this adapted CMfg model are designed. Finally, an evaluation is performed through a survey among 25 practitioners and 12 in-depth interviews with industry experts. The paper assesses the desirability and categorizes the benefits and barriers of such a CMfg platform for IC. The results suggest that CMfg could enhance the design quality, support IC suitability assessment for project developers and lower financial risks for off-site manufacturers.

One of the key challenges in fully embracing machine learning (ML) in civil and environmental engineering revolves around the need for coding (or programming) experience and for acquiring ML-related infrastructure. This barrier can be overcome through the availability of various platforms that provide automated and coding-free ML services, as well as ML infrastructure (in the form of a cloud service or software). Thus, engineers can now adopt, create, and apply ML to tackle various problems with ease and little coding. From this view, this paper presents a comparison of five automated and coding-free ML platforms: BigML, Dataiku, DataRobot, Exploratory, and RapidMiner on civil and environmental engineering problems. This comparison shows that although these platforms differ in their setup, services, and provided ML algorithms, all platforms performed adequately and comparably well to each other and to coding-based ML analyses. These findings denote the usefulness of coding-free ML platforms, which can lead to a brighter future for ML integration into our domain.

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.