Abstract This paper introduces a framework for designing resilient deck‐to‐pier connections that aims to address bridge vulnerability to tsunami loading. This framework presents a novel application of displacement design methods. Analytical and numerical models were developed for use in the framework, which enable the proposed bridge deck‐to‐pier connection response to be characterized. The numerical model uses nonlinear tsunami pushover analyses to simulate the superstructure response, while the analytical model uses a simplified formulation of the monolithic beam analogy. Validation was conducted using experimental data, which demonstrated that both models could accurately replicate the response of mild steel, stainless steel, and glass fiber‐reinforced polymer connections under tsunami uplift. Finally, a study was undertaken using the numerical model to extend the conclusions of the experimental testing. The findings of this numerical study highlighted that these connections can be customized for different situations to optimize bridge resilience to tsunami loading.
We are developing and operating the laser hammering system (LHS) that is non-destructive remote sensing device for concrete infrastructures. LHS can digitize the hammering test which is current mainstream of the inspection of concrete by using an impact laser (short-pulse laser), laser Doppler vibrometer, scanning system and camera. In this study, long-range LHS (applicable distance of 30 m) and small LHS (160 kg weight, for use on aerial work platforms) are developed for the bridge inspection. Long-range system succeeded on measurement of the concrete specimens of class 2 and 3 defect which were identified by active inspectors, and real defect of class 2 on bridge on use at the distance of 30 m. Small LHS succeeded on generation the surface vibration of the concrete specimens of class 2 and 3 defect which were identified by active inspectors. It indicates Long-range LHS and small LHS satisfied the inspection requirement as the support tool of hammering test prescribed by Japanese government. These devices are available for support to inspectors and digitalization of concrete sound status to achieve smart infra-maintenance.
The rapid digitization of the real estate and architectural design industries has created a high demand for automated tools capable of parsing 2D raster floor plans. Traditional manual measurement and visual inspection are not only time-consuming but also highly susceptible to human error. In this paper, we propose a comprehensive, end-to-end deep learning framework designed to automatically extract rich semantic information from unstructured 2D floor plan images and provide professional design guidance via Large Language Models (LLMs). Our integrated pipeline employs the state-of-the-art YOLOv8 object detection model to accurately localize and classify 18 distinct architectural symbols and furniture items (e.g., doors, windows, beds, cupboards). Simultaneously, a U-Net architecture with a ResNet34 encoder is utilized for the precise semantic segmentation of structural elements, specifically walls and interior room spaces. To translate pixel-level predictions into actionable real-world metrics, we introduce a robust area calculation algorithm based on user-defined reference scale calibration. Furthermore, to bridge the gap between raw geometric data and actionable architectural intelligence, we introduce an LLM-driven evaluation module utilizing a local Ollama deployment and a Retrieval-Augmented Generation (RAG) pipeline to assess design compliance and quality. To overcome the scarcity of annotated architectural datasets, we implement a systematic data augmentation strategy, expanding a core dataset of 101 manually annotated floor plans to 303 varied instances, thereby significantly enhancing model generalization. Experimental results indicate that our YOLOv8-based detection module achieves a mean Average Precision (mAP50) of 92.3%, while the U-Net segmentation module achieves a mean Intersection over Union (mIoU) of 95.71%. Furthermore, the integrated system is deployed as a user-friendly, interactive web application, acting as an intelligent architectural assistant and demonstrating its practical viability and high efficiency for real-world engineering and architectural applications.
ABSTRACT This study proposes a new seismic design procedure for mid‐story isolated structures that reduces design complexity, computational effort, and time. In the proposed procedure, referred to as the coupling coefficients (CCs) method, the isolated superstructure and the substructure are initially treated as decoupled and analyzed separately. CCs are then used to capture the interaction effects among seismic isolation, substructure, and superstructure. We first derived the CCs in closed form using basic concepts of modal analysis. Then, we validate these analytical expressions using statistical results obtained through comprehensive numerical simulations. We described the dynamic problem using mass and stiffness ratios. For stiffness ratios () limited to , and stiffness‐to‐mass ratios ( t ) limited to , simplified expressions for modal periods, masses, participation factors, and damping ratios are developed. A reduced damping in the first mode captures the amplification effects of the substructure on the isolated superstructure. An increased damping in the second mode captures the tuned mass damper effects of the isolated superstructure on the substructure. For small stiffness ratios, the seismic demand of the isolation system can be approximated by that of the independent isolated superstructure on the ground, and the demand of the substructure can be approximated by that of the independent substructure, with adjustments made only for the viscous damping effects.
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Read morePrognostics of the health degradation of lithium-ion batteries plays a crucial role in the electrification of transportation systems. Data-driven approaches have been widely adopted for battery health forecasting, but the need for labeled data and the domain-specific nature of the prediction model limit the deployment of these approaches in real-world applications. In this study, we unlock the potential of large language models as a generalized approach for lithium-ion battery state-of-health forecasting. We demonstrate the feasibility of applying large language models in a few-shot and zero-shot learning setups, where the model is capable of forecasting the battery health degradation given proper guided prompts without the need for fine-tuning. Extensive experiments are conducted to evaluate the prediction performance with various usage setups considered. The results indicate that both few-shot and zero-shot learning setups yield satisfactory performance with the lowest root-mean-square error of [Formula: see text] achieved. In addition, this research examines various future operational conditions provided in the prompt and their impact on the prediction performance. The findings of this study provide insights into the potential of large language models as a generalized approach for lithium-ion battery health prognostics.
Read moreConversion from native temperate deciduous Nothofagus spp. forests to exotic Pinus spp. plantations in South-Central Chile, can substantially alter soil carbon (C) and nutrient cycling. We assessed the biogeochemical shifts resulting from this forest conversion by monitoring carbon and nutrient dynamics at 10 paired adult native forest and plantation sites across contrasting soil types (5 sites), including litterfall, LAI, fine root biomass, and soil CO₂ efflux, over a period of three years. In addition, we quantified aboveground tree biomass and total C, N, and P stocks in trees and soils. C inputs were significantly higher in native systems than in plantations (5.17 ± 1.19 vs. 3.19 ± 0.99 Mg C ha⁻¹ year⁻¹, respectively). Native forests also exhibited higher total C losses through soil CO₂ efflux (-3.19 ± 1.68 Mg C ha⁻¹ year⁻¹) than pine plantations (-2.54 ± 1.52 Mg C ha⁻¹ year⁻¹), yet maintained a more favorable balance between inputs and losses, largely due to differences in the dominant C input pathways. Native forests showed greater fine-root production and deeper, denser rooting systems, whereas plantations were characterized by higher litterfall and faster decomposition rates, potentially accelerating carbon mineralization. Across soil types, native forests exhibited significantly higher aboveground biomass than adjacent plantations. Although aboveground carbon stocks tended to be higher in native forests, these differences were not statistically significant when all soil types were considered together. Nevertheless, native forests consistently allocated a greater proportion of carbon belowground, whereas plantations concentrated carbon inputs in aboveground compartments. Native forests also maintained larger soil and aboveground biomass N and P stocks than plantations. Responses varied systematically among soil types, with residual crystalline soils and older volcanic deposits showing higher carbon losses relative to inputs than younger volcanic ash-derived soils. Overall, our results demonstrate that soil parent material modulates post-conversion carbon fluxes and the distribution of ecosystem C, N, and P stocks, highlighting the importance of soil–vegetation interactions when evaluating carbon benefits and long-term sustainability of forest management strategies. • Plantations showed faster litter decomposition and carbon turnover than natural forests. • Fine-root production was consistently higher in native forests. • Soil parent material constrained the balance between carbon inputs and losses. • Soil type modulated post-conversion carbon fluxes and nutrient stocks.
Read moreConducting numerical modal validations of operational bridges are vital for their structural health monitoring (SHM). This type of validation fully depends on the accurate assessment of the dynamic characteristics of bridges. Accordingly, the arrangement and placement of sensors to measure vibration data also play a crucial role in ensuring the effective capturing of both global and localised dynamic behaviours. Hence, a proper sensor layout is required to accurately identify the bridge behaviour. This study investigates the effectiveness of different sensor configurations in identifying modal parameters of a case study bridge, located in New South Wales, Australia using three sensor layouts: full-width coverage and two partial-width configurations, each covering one side of the bridge. The performance of these configurations was evaluated through field testing and numerical validation. The results showed up to 97% similarity for the first mode shape under partial-width layouts. The partial-width configurations demonstrated high accuracy in capturing both global and localized modal responses and were effective in monitoring localized stress variations as they are aligned with traffic flow zones. However, the full-width layout is preferable for global structural assessments and showed limitations in detecting higher-order modes. This study underscores the critical importance of sensor placement in achieving accurate modal analysis and robust numerical validation while also highlighting the comparative advantage of integrating dynamic response metrics into sensor placement strategies.
Read moreThe Diffuse Galactic gamma-ray Emission (DGE), mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a crucial probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. Using the source-deduction method and the latest data of WCDA and KM2A, we have preliminarily measured this emission and present the energy spectra of diffuse emission in the Inner Galaxy region ( 15°< l <125°, |b| < 5°) and the Outer Galaxy region (125°< l < 235°, |b| < 5°). Additionally, we found that the spatial distribution of the diffuse emission deviates from the Planck Dust map, suggesting distinct astrophysical origins. These findings offer valuable insights into the properties of diffuse gamma-ray emissions and highlight the need for refined methodologies to better understand the underlying astrophysical processes.
Read moreContinuous-variable quantum key distribution (CVQKD) using passive state preparation (PSP) offers low-cost, high-rate secure communication. However, the existing PSP-CVQKD scheme with a transmitted local oscillator has high photon leakage noise and poor stability, making it unsuitable for high-loss transmission. In this work, for the first time, we propose and implement a local local oscillator (LLO) CVQKD system using a self-referenced (SR) PSP scheme, and give a theoretical proof of the equivalence of the PSP and GMCS protocol using temporal-mode theory. By employing the novel self-referenced pilot scheme to achieve high-precision time-varying frequency and phase compensation algorithms, we significantly improve the system' s signal-to-noise ratio and stability. The system achieves a record-high asymptotic secret key rate of 10.34 Mbps over a free-space channel with up to 23.5 dB loss, while maintaining low excess noise and robust performance under turbulent conditions. This work establishes the feasibility of SR-LLO CVQKD, providing a practical pathway toward secure, high-rate quantum communication in realistic environments.
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