Preventing urban regions from seismic wave destruction is of paramount significance because it is closely related to urban lifeline and prosperity of cities. Almost all conventional seismic resistance approaches rely on the stiffness and strength of buildings, which require excessive structural components with additional self-weights. In this study, we propose a pine-like seismic metamaterial for efficient attenuation of surface Rayleigh waves. The pine arrays in suburban regions demonstrate an ability to convert Rayleigh waves to shear bulk waves or attenuate waves on the surface via local resonation. This property originates from a gradient design of pine arrays, where a scale ratio is defined to tune the geometric properties of each pine unit. Specifically, a gradient pine array with a scale ratio smaller than one can interact with the Rayleigh waves and convert them to shear waves that propagate deep to underground. The transmission ratio of the entire system indicates a broadband wave attenuation at subwavelength scale. It reveals that the pine is able to couple with a certain elastic Rayleigh wave whose wavelength is much larger than the lattice constant, which is rarely achieved in any conventional civil engineering structures such as open trench barriers and filled trench barriers. Additionally, a numerical model of an urban region and suburban pine array is established and analyzed. Infrastructures and structures in a city that suffer direct Rayleigh wave interference run into a high risk of structural destruction as compared to urban structures protected with suburban metamaterial pine arrays. Finally, two real earthquake wave signals are used to validate the efficiency of the pine arrays in dissipating earthquake energy. The approach in this paper can be extended to deal with more complex naturally available structures for examining the elastic wave attenuation abilities of these novel structures.
Mainshock-aftershock earthquakes have gained significant attention since accumulated damages induced by multiple shocks are likely to cause failure of structures. This paper presents a deep learning approach based on a Gated Recurrent Unit (GRU) network for assessing the seismic fragility of structures under mainshock-aftershock scenarios. The GRU network is utilized to create a surrogate model that captures the nonlinear relationship between seismic responses and mainshock-aftershock earthquakes. Subsequently, seismic fragility analysis is conducted based on double incremental dynamic analysis, employing the trained GRU network. A single-degree-of-freedom system with Bouc-Wen hysteretic behavior was investigated to demonstrate the proposed approach. The results indicate that the approach shows a substantial reduction in computational costs and holds promising potential for evaluating the seismic fragility of structures exposed to mainshock-aftershock earthquakes.
Power cables play a very important role in urban development. On the other hand, the integrity and the safe operation of power cables are crucial as even a minor potential mishap can result in serious loss of life and property. Hence, early diagnosis and warning of cable faults are imperative. Addressing the current lack of practical detection technologies, this study proposes a non-destructive testing method based on electromagnetic field principles. Through scanning the power-on power cables with probes, the electric field intensity around the cables can be measured, and the weak anomaly caused by structural and material defects can be detected using Superlets transformation. Additionally, to gain a better characterization of the faults, a digital post-processing approach consisting of imaging and sonification algorithms is developed to aid in pinpointing the location of the faults. Both numerical simulation and experimental test indicate that the proposed non-destructive detection method is feasible and can achieve good accuracy in locating cable faults. With image and audio characterization, the present method has great potential applications in ensuring the safety of power cables.
Structural integrity is essential for safety in infrastructure, as it can help prevent catastrophic failures and financial losses. The significance of vibration-based damage detection has grown substantially in fields such as civil and mechanical engineering. Concurrently, the advancements in computational capacities have facilitated the integration of machine learning into damage detection processes through post-processing algorithms. Nevertheless, these require extensive data from structure-affixed sensors, raising computational requirements. In an effort to address this challenge, we propose a novel approach utilizing a pre-trained convolutional neural network (CNN) based on images to identify and assess structural damage. This method involves employing wavelet transform and scalograms to convert numerical acceleration data into image data, preserving spatial and temporal information more effectively compared to conventional Fourier transform frequency analysis. Six acceleration data channels are collected from carefully chosen nodes on a mini bridge model and a corresponding finite element bridge model, to train the CNN. The efficiency of training is further enhanced by applying transfer machine learning through two pre-trained CNNs, namely Alexnet and Resnet. We evaluate our method using different damage scenarios, and both Alexnet and Resnet show prediction accuracies over 90%.
The development of Zambia has brought increased large-scale infrastructure construction that has necessitated the need for improved foundation techniques that are both economical and adequate in capacity. Clay and soft soils with low bearing capacities and high compressibility could render structural foundations to perform poorly and shorten the design life of the bridges and structures. This study used a bridge case study in Northern Province, Zambia to investigate the use of geosynthetic encased columns (GECs) to support the bridge embankments to reduce differential settlements. End bearing fully encased columns were compared to floating columns of varying lengths by numerical modelling in PLAXIS 3D. The Hardening Soil and Mohr–Coulomb soil models were used for the column surrounding soil and the GECs in the finite element analysis. The results showed that the end bearing columns had the least differential settlements at the soil surface, whilst the reduction in floating column length increased the punching settlements. Moreover, the shear stress along the interface of the GECs and surrounding soil varied from 20 kN/m2 to 142 kN/m2, where the end bearing GEC had the least shear stress.
This study presents the construction of an urban underground sensing system using distributed acoustic sensing (DAS) technology, which utilizes the existing optical fiber infrastructure around urban roads for communication. To address the challenges posed by the complexity and variability of DAS data in infrastructure monitoring environments such as urban roads, as well as the difficulty and poor effectiveness of raw data visualization, a novel method for visualizing DAS data is proposed. This method involves preprocessing the data through wavelet threshold denoising, combining it with the root-mean-square (RMS) energy index to generate a visualization, and applying the dynamic threshold method to remove and suppress abnormal data indicators. Finally, this paper tested the visualization performance to assess the effectiveness of the proposed method in improving urban road safety management. The study focused on three typical urban road safety risk events: vehicle driving, construction, and road subsurface cavity incidents. The results demonstrate the efficacy of the data visualization method, showing improved visualization of vehicle trajectory directions and numbers, construction segment behaviors, and approximate road subsurface cavity locations in the time domain compared to the original data.
Corrosion is one of the most common forms of damage to pressure pipes. CIPP liner can improve the mechanical properties of the pipe with corrosion. It is very important to study the mechanical response of corrosion pipes rehabilitated by CIPP liner. In this study, a three-dimensional finite element model of a pressure pipe rehabilitated with CIPP liner under the condition of circular corrosion is established, and the model is evaluated by using the experimental and analytical results. Afterward, the influence of each parameter on the stress and displacement of the liner is analyzed. Finally, the liner stress calculation equation is obtained based on 870 sets of data. The results show that the finite element results are in good agreement with the experimental and analytical results. The maximum stress of the liner first occurs at the center of the corroded circle, and as the corrosion diameter increases, the maximum stress gradually shifts to the edge. The maximum stress of the liner increases first and then tends to be stable with the increase of the corrosion diameter, and the friction coefficient has little effect on the liner stress.
Buried pipeline systems are vulnerable to joint damage in earthquakes. Previous studies have shown that bellows joints are effective in increasing deformation capacity and reducing the axial force of the main pipeline. However, the effectiveness of bellows joints in enhancing the deformation capacity of the pipelines is affected by the location of the crossing fault and the soil at the site. This paper constructs and validates a finite element model of a buried steel pipeline connected by a bellows joint using ABAQUS to investigate this issue. The effects of fault location, soil properties, and internal pressure on the bellows joint connected pipelines were analyzed using a finite element model. The results indicate that the bellows joints exhibit the best energy dissipation and deformation enhancement when the fault passes directly through the joints. Bellows joints in soft soil are better at dissipating energy, reducing pipe deformation, and mitigating pipe damage compared to those in hard soils. The internal pressure is helpful to increase the mechanical properties of the pipelines. The findings can provide some guidance for pipeline design and mitigation strategies for ensuring the reliability and safety of buried pipeline systems in earthquake-prone areas.
The development of the Internet of Things has led to a significant increase in the number of devices, consequently generating a vast amount of data and resulting in an influx of unlabeled data. Collecting these data enables the training of robust models to support a broader range of applications. However, labeling these data can be costly, and the models dependent on labeled data are often unsuitable for rapidly evolving fields like vehicular networks and mobile Internet of Things, where new data continuously emerge. To address this challenge, Self-Supervised Learning (SSL) offers a way to train models without the need for labels. Nevertheless, the data stored locally in vehicles are considered private, and vehicles are reluctant to share data with others. Federated Learning (FL) is an advanced distributed machine learning approach that protects each vehicle’s privacy by allowing models to be trained locally and the model parameters to be exchanged across multiple devices simultaneously. Additionally, vehicles capture images while driving through cameras mounted on their rooftops. If a vehicle’s velocity is too high, the captured images, donated as local data, may be blurred. Simple aggregation of such data can negatively impact the accuracy of the aggregated model and slow down the convergence speed of FL. This paper proposes a FL algorithm for aggregation based on image blur levels, which is called FLSimCo. This algorithm does not require labels and serves as a pre-training stage for SSL in vehicular networks. Simulation results demonstrate that the proposed algorithm achieves fast and stable convergence.
• Two-layer optimization framework for rescue guiders layout is presented.
• The multi objective optimization model is solved based on improved NSGA-II algorithm.
• The optimal number and layout of guiders is obtained through iterative optimization.
The combination of fingerprint positioning and 5G (the 5th Generation Mobile Communication Technology) offers broader application prospects for indoor positioning technology, but also brings challenges in real-time performance. In this paper, we propose a fingerprint positioning method based on a deep convolutional neural network (DCNN) using a classification approach in a single-base station scenario for massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation. The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. The positioning performance is simulated in a DeepMIMO indoor environment, showing that the classification DCNN method reduces the positioning time by 77.05% compared to the non-classification method, with only a 1.08% increase in average positioning error.
The seismic failure mechanism of underground structures showing structural collapse is attributed to the deformation incompatibility between the central columns and the sidewalls, especially the insufficient deformation capacity of the central columns. Therefore, the approaches to improve the seismic performance of underground structures mainly aim at avoiding the damage of central columns. This paper presents a comparative study on the seismic performance of underground structures with the central columns retrofitted with different numbers of Carbon Fiber Reinforced Plastics (CFRP) layers. Seismic capacity of the CFRP retrofitting reinforced concrete (RC) columns was explored in detail through experimental and numerical approaches. Then numerical models were built and verified to simulate the behaviors of the CFRP retrofitting RC columns, and the seismic performance of the CFRP retrofitting underground structures was numerically simulated. Based on the numerical results, the damage of the CFRP retrofitting underground structures was calculated, and damage classification illustrated that the seismic performance of underground structures was enhanced remarkably. Finally, parametric studies were conducted to discuss about how the number of CFRP layers and the earthquake intensity affect the earthquake-induced damage of underground structures. Conclusions from this study could be referenced for seismic retrofitting of existing underground structures.
The research on multi-robot systems has been divided into various fields, such as communication, navigation, task allocation, and collaborative transport. While significant progress has been made in each area, there has been limited research integrating these fields to build a fully autonomous multi-robot collaborative transport system. Therefore, we identify the key issues and propose a multi-robot collaborative transport system founded on ROS1 and conduct validation in a simulated environment, laying a solid foundation for the system to run on real robots. The primary contributions of this study include three key areas: (1) modeling and validating robot collaborative transport, (2) developing a visual task allocation system leveraging FastDDS service, and (3) resolving path collision issues in multi-robot navigation through both traditional methods and reinforcement learning techniques. Extensive experimental evaluations demonstrate that the proposed intelligent multi-robot collaborative transport system can autonomously navigate to target points for collaborative transport task. Performance assessments, based on the error between the target point and the object’s arrival point as well as the transport trajectory error, reveal that the system effectively completes the assigned tasks.
The primary goal of Structural Health Monitoring (SHM) is to assess performance and determine the physical state of the structures. Technological improvements and the ubiquitous accessibility of Wi-Fi networks have enhanced real-time SHM based on the Internet of Things (IoT). Extensive structural health evaluation can be conducted using real-time test data collected from various IoT sensors on civil infrastructures. These sensors monitor multiple structural health parameters, and the data is accessible via cloud-based storage platforms. This paper presents an overview of IoT technologies and provides an extensive literature review of IoT applications for civil infrastructures, highlighting associated challenges.
This study investigates the feasibility and efficacy of Flexible Fiber-reinforced Polypropylene (FFPP) thermoplastic lining technology for the rehabilitation of concrete pipelines, specifically focusing on BONNA pipes. A custom-built test bench, featuring a 2-m high platform and multiple 90° bends, was designed to simulate the impact of pipe gallery space, adaptability and material accessibility of bent pipes, and cooling issues of long-distance dragging of materials. The simulation process utilizes a modified polyvinyl chloride (PVC) liner with unique thermomechanical properties. The liner, folded into an "H" shape, is mechanically inserted into the host pipe using a 2-ton winch and pulley system. During insertion, continuous high-temperature steam injection softens the material, facilitating expansion and conformity to the pipe's internal surface. Subsequently, cold air application rigidifies the liner below 60 °C while maintaining pressure, ensuring structural integrity and adherence to the pipe wall. Results revealed that while the FFPP liner successfully navigated through confined spaces, including a 300 mm expansion joint, spatial constraints led to localized cracking defects during inflation. Traction feasibility tests using a 2-ton winch demonstrated high pulling resistance in sections with multiple bends. The liner exhibited excellent adhesion in straight pipe sections but showed significant wrinkling and poor adhesion in 90° bends. Notably, the liner demonstrated remarkable strength, withstanding internal pressures exceeding 3.3 MPa in a DN300 pipe with a 10 cm diameter intentional defect, far surpassing the on-site hydrostatic test pressure of 9 bar. This study addresses a significant gap in trenchless rehabilitation research by evaluating the FFPP thermoplastic lining technique in complex pipeline geometries, an area previously understudied. While the technique shows promise for structural reinforcement in straight pipe sections, our findings reveal that its application in complex pipeline geometries requires further refinement. The study contributes to the field of trenchless pipeline rehabilitation in several ways: (1) it provides empirical data on FFPP liner performance in multi-bend configurations and confined spaces, (2) it identifies specific challenges such as localized cracking and poor adhesion in bends, and (3) it demonstrates the liner's exceptional strength under high pressure conditions. These insights advance our understanding of FFPP technology's potential and limitations in concrete pipe repair, paving the way for future research and development in optimizing trenchless rehabilitation techniques for complex pipeline systems.
As a type of critical transportation infrastructures, safety of major tunnels in urban transportation corridors has become unprecedentedly crucial. In various fire incidents with high temperature environment, the ceiling and wall of a tunnel would risk spalling, strength reduction, and even structural failure. In addition to risks of structural damage, high temperature and thick smoke in fire incidents will increase the risk of severe injury and even death due to burning, lack of oxygen, inhaling smoke and reduced visibility for people inside the tunnel. Limited mobility of evacuees would delay the evacuation process by endangering the safety of both evacuees and first responders. A comprehensive simulation-based case study is conducted to look into the fire and smoke simulation as well as potential threats to structural integrity and human health caused by fire in typical traffic incidents of three types of vehicles. Fire and smoke simulation are conducted using the Fire Dynamics Simulator (FDS), followed by the thermal and structural analyses with ANSYS. The comprehensive parametric study provides insights in terms of the impacts of different parameters including various typical traffic fire incidents on the structural performance and potential threats to evacuees.
Bridge widening involves phased construction of adjacent structures to maintain uninterrupted traffic flow. This process exposes freshly placed longitudinal joints between staged deck constructions to vehicle-induced vibrations, potentially compromising their mechanical integrity. This study investigates the flexural behavior of ultra-high-performance concrete (UHPC) longitudinal joints under such vibrations through model tests. To simulate actual site conditions, we developed a novel vibration test setup that replicates the dynamic environment experienced by these joints during construction. Micro- and meso-scale tests were conducted to examine the flexural behavior of longitudinal joints following vibration exposure. Results revealed that vibration amplitude significantly influences fiber orientation and flexural strength of ultra-high-performance concrete (UHPC) wet joint specimens. Low-amplitude vibrations (3 Hz at 1 mm and 3 mm) enhanced fiber orientation, increasing flexural strength by 11.5% to 19.8% and ultimate load capacity by 17% compared to non-vibrated specimens. Conversely, high-amplitude vibrations (3 Hz at 5 mm) adversely affected fiber orientation, decreasing flexural strength by 23.9% and ultimate load capacity by 19% relative to non-vibrated specimens.
Traffic flow mobility on expressway plays an important role in urban development. With the emergent technologies, connected vehicles, including both connected automated vehicles (CAVs) and connected regular vehicles (CRVs), are equipped with connectivity features to improve efficiency of urban expressway in a mixed traffic scenario. Existing research indicates that without targeted management, the integration of CAVs and CRVs into regular vehicles (RVs) traffic can lead to a series of congestion issues. A potential solution lies in the implementation of dedicated lanes, each designated for specific vehicle types, which could alleviate traffic flow complications. Therefore, this paper proposes a dynamic optimal lane management strategy for multi-lane mixed traffic urban expressway, aimed at maximizing the whole discharge flow. To begin with, we present an analytical method to mathematically derive discharge flow of each lane type including mixed traffic lane, CAV/CRV dedicated lane, and RV dedicated lane, from the perspective of both traffic demand and traffic capacity. Then, taking a three-lane mixed traffic urban expressway as an application, we conduct a numerical analysis of the dynamic optimal lane management strategy, based on comparisons among eight distinct strategies, under varying factors of traffic demands, penetration rates of CAVs and CRVs, and allocation proportions of vehicles upstream assigned to mixed traffic lanes. The results validate the effectiveness of the analytical method proposed for lane management strategies. It also indicates that the aforementioned system factors have significant impacts on the dynamic optimal lane management strategy.