Bridging Framework Research Articles

Reliability-Centric Maintenance Planning for Bridge Infrastructure: A Novel Method Based on Improved Electric Fish Optimization

Bridge infrastructure provides an important effect on contemporary transportation networks, and its upkeep is significant for ensuring public safety and reducing economic impacts. Nevertheless, the aging and degradation of bridge structures present considerable challenges for asset managers, who must navigate the necessity of maintenance against constrained financial resources. Conventional maintenance approaches typically emphasize reactive repairs, which can result in elevated lifecycle expenses and risk structural integrity. This paper introduces an innovative framework aimed at optimizing bridge maintenance expenditures while maintaining structural safety. The proposed methodology incorporates a reliability-based deterioration model, an intervention effect model, a financial model, and an optimization model empowered by an Improved Electric Fish Optimization (IEFO) algorithm. The framework is demonstrated through a case study of a reinforced bridge framework designed according to the standards of Canadian highway bridge design. The findings illustrate that the proposed methodology can substantially lower lifecycle costs by investigating the most economical maintenance strategies, including minor repairs that can postpone the necessity for expensive major interventions. The optimal scenario identified by the IEFO algorithm yielded lower equivalent uniform annual costs in comparison with the traditional scenario focused solely on major repairs. This research advances the field of data-driven maintenance planning for bridge infrastructure, empowering asset managers to make well-informed decisions that effectively balance cost and safety considerations.

Study on increasing load capacity of wooden arch bridge by CFRP strengthening: experimental and numerical Verification

The wooden arch corridor bridge is a typical representative of Chinese wooden bridges, holding significant historical research value. Currently, these bridges face issues of severe component deformation and insufficient load-bearing capacity. To address these problems, this study employs CFRP reinforcement on the components of wooden arch corridor bridges to reduce deformation and enhance load-bearing capacity. Experimental research on CFRP reinforcement has yielded the elastic modulus of the bonding interface. Given the lack of an accurate numerical model for wooden arch corridor bridges, this study establishes a precise numerical model by setting parameters based on load test data from wooden arch corridor bridges. The elastic modulus obtained from the experiments is input into the numerical model for analysis. The results indicate that CFRP exhibits excellent reinforcement performance, with the load-bearing capacity of the reinforced damaged components still reaching 75%–85% of their original capacity, while the load-bearing capacity of the reinforced undamaged components increases to 130%–140% of their original capacity. The failure modes of the CFRP-reinforced wooden components suggest that allowing for some gaps in the bonding of CFRP can enhance overall ductility. The application of CFRP to wooden arch corridor bridges also demonstrates favorable reinforcement effects, increasing the load-bearing capacity of the arch surface by approximately 20%, thereby providing a theoretical basis for the reinforcement of wooden arch bridge frameworks.

Enhancing structural damage evaluation of PC girder bridges through digital twinning Bayesian model updating

Developing a unified analytical framework to assess the structural performance of deteriorated prestressed concrete (PC) girder bridges is important for their maintenance. This study aims to construct a physics-based digital twin framework for PC girder bridges incorporating Bayesian model selection and updating, a high-fidelity three-dimensional (3D) finite element (FE) model, and a method for simulating prestressed tendon damage. In this study, three PC girder specimens were designed and tested to evaluate the effects of tendon rupture near the anchorage region on structural performance. Bayesian model selection was used to determine the most plausible model class based on the estimated model evidence. The most probable values (MPVs) of the model parameters, derived from the estimated posterior probability density functions (PDFs), were then used to calibrate the FE model, which reflects a healthy state of the PC girder bridge. This updated FE model serves as the baseline for conducting what-if scenario simulations. Subsequently, nonlinear segments of the constitutive model, obtained from coupon tests, and a simulation method for tendon rupture were incorporated into the updated FE model. Nonlinear static simulation results obtained through the updated model were found to be consistent with both the observed crack pattern and the load–deflection curve. The reliability of the digital twinning FE model for assessing tendon damage and for predicting the residual load-bearing capacity was verified through comparison with measured data. Therefore, the digital twinning Bayesian model updating approach shows potential applications in continuous structural health monitoring (SHM) of PC bridges.

Uncovering a general oxidation model of nitrogen-containing oxidant in N-heterocyclic carbene (NHC) catalyzed oxidative reactions of aldehydes

Predicting how oxidants interact with reactive partners to alter the oxidation mechanism is a significant challenge in carbene catalysis. In this study, we investigated several examples to address this issue. Using density functional theory (DFT), we explored N-heterocyclic carbene (NHC)-catalyzed oxidative [2 + 4] annulations of aliphatic aldehydes with α,β-unsaturated ketones, focusing on the role of nitrogen-containing oxidant. Our computational results reveal that a hydrogen-bonding bridge framework, formed during oxidation, is crucial for facilitating π···π stacking interactions between the oxidant and the NHC catalyst. These interactions significantly lower the oxidation energy barrier, enabling a facile hydride transfer. This mechanism was validated across other reactions involving different nitrogen-containing oxidants. Frontier molecular orbital (FMO) analysis demonstrates how the NHC catalyst lowers the cycloaddition energy barrier by altering the orbital overlap from shoulder-to-head to head-to-head. Additionally, this elucidates the origin of stereoselectivity by highlighting energy differences between two reacting components at cycloaddition transition states. The nucleophilic index further predicts the preferred ketone attack site. This work advances our understanding of oxidation mechanisms involving nitrogen-containing oxidants and offers valuable insights for designing potent organic oxidants in organocatalytic oxidative reactions.

Atomistic Simulation of HF Etching Process of Amorphous Si3N4 Using Machine Learning Potential.

An atomistic understanding of dry-etching processes with reactive molecules is crucial for achieving geometric integrity in highly scaled semiconductor devices. Molecular dynamics (MD) simulations are instrumental, but the lack of reliable force fields hinders the widespread use of MD in etching simulations. In this work, we develop an accurate neural network potential (NNP) for simulating the etching process of amorphous Si3N4 with HF molecules. The surface reactions in diverse local environments are considered by incorporating several types of training sets: baseline structures, reaction-specific data, and general-purpose training sets. Furthermore, the NNP is refined through iterative comparisons with the density functional theory results. Using the trained NNP, we carry out etching simulations, which allow for detailed observation and analysis of key processes such as preferential sputtering, surface modification, etching yield, threshold energy, and the distribution of etching products. Additionally, we develop a simple continuum model, built from the MD simulation results, which effectively reproduces the surface composition obtained with MD simulations. By establishing a computational framework for atomistic etching simulation and scale bridging, this work will pave the way for more accurate and efficient design of etching processes in the semiconductor industry, enhancing device performance and manufacturing precision.

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing

Single-cell sequencing is frequently affected by “omission” due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly “omitted” cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of “omitted” cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

Seismic response prediction and fragility assessment of high-speed railway bridges using machine learning technology

Nowadays, reliable and accurate prediction of the seismic response of high-speed railway bridges and then rapid assessment of their seismic fragility is crucial for the risk assessment of regional high-speed railway bridges. However, the traditional seismic fragility assessment based on nonlinear time history analysis (NTHA) has a characteristic of high computational cost. Therefore, this study explores different machine learning (ML) methods to develop a reliable rapid seismic fragility assessment framework for high-speed railway bridges. Taking a typical high-speed railway continuous (HRC) bridge as a case, a large number of numerical model and ground motion pairs considering various structural and ground motion features are established. Then, the two types of models including six popular ML methods are investigated, in which Lasso regression, artificial neural network (ANN) and support vector machine (SVM) are single models, and random forest (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (Light GBM) are ensemble models. A database containing a large number of seismic responses of different components is established through the NTHA to develop prediction models, and the optimal hyperparameters of different ML models are determined by ten-fold cross-validation and grid search. The study results show that the ensemble model can reliably predict the seismic response of the HRC bridge compared with the single model, and the XGBoost model is recommended for seismic fragility assessment. On this basis, the optimal ML model obtained by the performance evaluation is applied to develop seismic fragility curves of the HRC bridge, which are compared with the fragility curves based on NTHA. The results indicate that the XGBoost model can effectively replace the traditional NTHA for rapid seismic fragility assessment of the HRC bridge, which can achieve a sufficient assessment level while saving calculation costs.

Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order–disorder transitions in Li[formula omitted]CoO[formula omitted]

LixTMO2 (TM=Ni, Co, Mn) forms an important family of cathode materials for Li-ion batteries, whose performance is strongly governed by Li composition-dependent crystal structure and phase stability. Here, we use LixCoO2 (LCO) as a model system to benchmark a machine learning-enabled framework for bridging scales in materials physics. We focus on two scales: (a) assemblies of thousands of atoms described by density functional theory-informed statistical mechanics, and (b) continuum phase field models to study the dynamics of order–disorder transitions in LCO. Central to the scale bridging is the rigorous, quantitatively accurate, representation of the free energy density and chemical potentials of this material system by coarse-graining formation energies for specific atomic configurations. We develop active learning workflows to train recently developed integrable deep neural networks for such high-dimensional free energy density and chemical potential functions. The resulting, first principles-informed, machine learning-enabled, phase-field computations allow us to study LCO cathodes’ order–disorder transitions in terms of temperature, microstructure, and charge cycling. We highlight several insights gained to the dynamics of the phase transitions, and that have been made possible by the quantitatively rigorous scale bridging. To the best of our knowledge, such a scale bridging framework has not been previously demonstrated for LCO, or for materials systems of comparable technological interest. This approach can be expanded to other materials systems and can incorporate additional physics to that studied here.

Small target disease detection based on YOLOv5 framework for intelligent bridges

Small target disease detection based on YOLOv5 framework for intelligent bridges

Bridging knowledge distillation gap for few-sample unsupervised semantic segmentation

Due to privacy, security, and costly labeling of images, unsupervised semantic segmentation with very few samples has become a promising direction, but still remains unexplored. This inspires us to introduce the few-sample unsupervised semantic segmentation task, which is very challenging because generalizing the segmentation model from only a few unlabeled images is far from sufficient. We address this problem in the knowledge distillation perspective, by proposing a medium-sized auxiliary network as the bridge, which narrows down the semantic knowledge gap between teacher network (large) and student network (small). To this end, we develop the Knowledge Distillation Bridge (KDB) framework for few-sample unsupervised semantic segmentation. In particular, it consists of the teacher-auxiliary-student architecture, which adopts the block-wise distillation that encourages the auxiliary to imitate the teacher and the student to imitate the auxiliary. In this way, the knowledge gap between the source feature distribution and the target one is reduced, allowing the student with the smaller network to be readily deployed in highly-demanding environment. Meanwhile, each channel characterizes different semantics in feature map, which motivates us to distill the features of decoder in a channel-wise manner. Extensive experiments on two benchmarks including Pascal VOC2012 and Cityscapes demonstrate the promising performance of the proposed method, which strikes a good balance between precision and speed, e.g., it achieves the inference speed of 230 fps for a 512×512 image.

An accurate and low-cost vehicle-induced deflection prediction framework for long-span bridges using deep learning and monitoring data

Accurate vehicle-induced deflection prediction is critical to bridge structural management and maintenance. However, most existing studies use finite element models and weigh-in-motion (WIM) system to predict the deflection of bridges, the cost of building finite element models is expensive. An accurate and low-cost vehicle-induced deflection prediction framework is developed in this study. In this framework, the calculation method of the spatio-temporal distribution matrix of the vehicle loads is proposed. A fusion attention mechanism bidirectional long short-term memory (ATT-BiLSTM) model is used to predict vehicle-induced deflection. Taking a suspension bridge in China as an example, the accuracy of short- and long-term vehicle-induced deflection prediction is tested, and different deep learning models are used for comparison. The results show that the proposed framework can predict short-term vehicle-induced deflection with a mean absolute error (MAE) of less than 2 mm, which can also accurately predict the extrema of vehicle-induced deflection within a certain time window. The proposed framework is independent of finite element models and lowers the cost of bridge deflection prediction.

Lignin Derived Ultrathin All-Solid Polymer Electrolytes with 3D Single-Ion Nanofiber Ionic Bridge Framework for High Performance Lithium Batteries.

The lignin derived ultrathin all-solid composite polymer electrolyte (CPE) with a thickness of only 13.2µm, which possess 3D nanofiber ionic bridge networks composed of single-ion lignin-based lithium salt (L-Li) and poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) as the framework, and poly(ethylene oxide)/lithium bis(trifluoromethanesulfonyl)imide (PEO/LiTFSI) as the filler, is obtained through electrospinning/spraying and hot-pressing. t. The Li-symmetric cell assembled with the CPE can stably cycle more than 6000h under 0.5mA cm-2 with little Li dendrites growth. Moreover, the assembled Li||CPE||LiFePO4 cells can stably cycle over 700 cycles at 0.2 C with a super high initial discharge capacity of 158.5 mAh g-1 at room temperature, and a favorable capacity of 123 mAh g-1 at -20°C for 250 cycles. The excellent electrochemical performance is mainly attributed to the reason that the nanofiber ionic bridge network can afford uniformly dispersed single-ion L-Li through electrospinning, which synergizes with the LiTFSI well dispersed in PEO to form abundant and efficient 3D Li+ transfer channels. The ultrathin CPE induces uniform deposition of Li+ at the interface, and effectively inhibit the lithium dendrites. This work provides a promising strategy to achieve ultrathin biobased electrolytes for solid-state lithium ion batteries.

A satellite-field phenological bridging framework for characterizing community-level spring forest phenology using multi-scale satellite imagery

Forest phenology, as a sensitive indicator of a forest’s response to climate change and variability, has long been monitored using remote sensing, yet has seldom been interpreted or validated with spatially compatible, community-level field phenological observations. In temperate deciduous forests, multiple spring phenological phases are critical for modeling carbon storage and biogeochemical cycles. The simultaneous detection of all these critical phenological phases at the community level remains a long-standing challenge. To tackle these challenges, the objective of this study is to develop a novel satellite-field phenological bridging framework for characterizing all key spring phenological phases of a fragmented deciduous forest using multi-scale satellite time series. The framework consists of four key components: deep learning-based spatiotemporal image fusion, satellite-based forest phenology modeling, satellite-based forest phenological metric extraction, and field-based forest community phenological characterization. With the devised framework, we extract a total of 24 satellite phenological metrics of Trelease Woods, a forest fragment near Urbana, IL, USA. From weekly field phenological observations of 148 canopy trees of 15 common species of the forest community, we devise three summative field phenological indices to characterize community-level phenological states in spring. Under the satellite-field bridging framework, events during each key spring forest phenological phase (i.e., bud swell, budburst/leafing out, leaf expansion, and leaf maturity) are successfully detected using the fusion imagery (MAE from 1.1 to 2.9 days and bias from -2.4 to 1.5 days). However, the satellite detection of the earliest field events may be influenced by understory plants, soil background, and snow. The subsequent multi-scale, satellite phenological analysis underscores the importance of taking into account spatial scale and representation from both satellite and field phenological perspectives in building corresponding bridging relationships. Among the extracted pheno-metrics, bridging the threshold-based metrics to field phenological indices results in the highest accuracy (MAE less than 3 days and bias less than 2 days). The strong agreement among the field indices demonstrates the effectiveness of our field phenological surveying approach in generating community-wide forest phenological representation. Our study innovatively scales up the field phenological observations from the individual trees to the species to the community level, and the devised framework enables accurate retrieval of all key phenological events of community-wide, spring canopy development of the forest fragment.

Bamboo's evolution as a sustainable material: a comprehensive exploration and practical integration as a structural element in concrete, bridge frameworks, and architectural perspectives

Bamboo's evolution as a sustainable material: a comprehensive exploration and practical integration as a structural element in concrete, bridge frameworks, and architectural perspectives

Realizing the Solidarity Dividend: A New Story for Educational Leadership

ABSTRACT Long Island has long maintained the most segregated suburban school system in America. In this case study, we used McGhee’s zero sum vs. solidarity dividend framework to qualitatively examine a group of 42 leaders’ perceptions of school segregation on Long Island as well as how they imagined possible solutions. Findings show that most leaders believed that the current system is unfair but unchangeable, and did not put forth possible solutions, nor, crucially, challenge the underlying zero sum, meritocratic status quo. However, we also highlight examples of leaders who espoused the need to create bridges among racially diverse districts and communities, acting in different ways to uplift the ideals of solidarity. We offer a three-part bridging framework that moves from rhetorical bridging, naming disconnectedness and its harms; through worldview bridging, connecting disparate racialized realities; to community bridging, actively stepping into new relationships and shared spaces. This research shows how important community-facing culturally responsive school leadership is when setting the narrative about racial inclusivity and integration, as well as in shaping the policy reform process. Instead of waiting for necessary top-down changes, we argue that leaders must act now to bring worldviews and communities together for the common good.

Building Research Infrastructure to Develop Greater Learning Efficiencies (BRIDGE).

In this manuscript, we outline our developed version of a Learning Health System (LHS) in oncology implemented at the Department of Veterans Affairs (VA). Transferring healthcare into an LHS framework has been one of the spearpoints of VA's Central Office and given the general lack of evidence generated through randomized control clinical trials to guide medical decisions in oncology, this domain is one of the most suitable for this change. We describe our technical solution, which includes a large real-world data repository, a data science and algorithm development framework, and the mechanism by which results are brought back to the clinic and to the patient. Additionally, we propose the need for a bridging framework that requires collaboration between informatics specialists and medical professionals to integrate knowledge generation into the clinical workflow at the point of care.

A physics-informed auto-encoder based cable force identification framework for long-span bridges

Cable force identification is crucial for ensuring the safety and operational performance of in-service long-span bridge structures. Besides the commonly-used frequency measurements for calculating cable forces using frequency-cable force relationship formulas, more efficient and straightforward identification could be achieved by directly utilizing frequency response functions (FRFs). This study presents a novel approach that employs neural networks to model the relationship between the FRFs and cable forces, resulting in a more streamlined method for identifying cable forces on long-span bridges. Firstly, the working mechanism of an auto-encoder is merged with the unique characteristics of the FRFs, giving the cross signature assurance criterion. This criterion is then integrated into the loss function as a constraint to account for the poor interpretability of pure data-driven methodology in solving engineering problems, leading to a grey-box data-driven paradigm. Following this paradigm, a physics-informed auto-encoder (PIAE) network is employed to reduce the dimensionality of the FRF data during extracting key features. The reduced FRF data are paired with the cable forces to form training samples. The PIAE network is then trained directly on these samples for the purpose of cable force identification. Finally, the validation of the proposed method was conducted on the actual monitoring data from a cable-stayed bridge and a concrete-filled steel tubular arch bridge. Results indicate that the proposed method achieves not only high prediction accuracy, but also a good fit between the predicted and actual developmental trends of cable forces, and is well-suited for the different types of bridges.

Fragility Analysis Framework for Bridges Subjected to Successive Natural Hazards

Bridges are key parts of transportation networks and hence, their post-hazard condition is vital for the recovery of the regions affected by natural hazards. However, the fact that they are often subjected to more than one destructive natural hazard during their life span resulting in accumulated damage that may influence their capacity has attracted relatively little research interest, while almost no design standards take it into consideration. The multiple hazard events may be successive single events, separated by considerable time intervals (e.g. an earthquake followed by a flood event), combined or cascading multiple-hazard events (e.g, landslide after an earthquake), or even simultaneous events. The main objective of this study is to propose an effective framework for the development of multiple-hazard fragility curves of bridges considering the effect of subsequent events and taking into account different possible damage scenarios of the bridge critical components, i.e. piers, bearings and the abutment-backfill systems. In this framework, the effect of the damage level of the critical components and their interdependence on the fragility assessment for multiple events is examined and compared with the bridge fragility for single events. A pilot case study is also included in the present work, aiming to highlight the effect of the initial damage state on the final fragility estimate of a typical riverine bridge.

Acommunity‐drivensocial knowledge management framework for bridgingrural–urbanknowledge divide using social technology

AbstractThis article illustrates how a social knowledge management (SKM) platform can be conceptualized, developed and implemented that can provide users with multi‐community connectivity, making it quicker and easier for members to connect, communicate, collaborate, and learn. By conducting case studies on rural craft producers in West Bengal, India, we have demonstrated how this platform fosters meaningful collaboration among rural artisans and various stakeholders within the craft production system, ultimately resulting in the formation of vibrant virtual communities. The platform promotes purposeful knowledge exchanges among variety of agents, which helps to reduce the knowledge divide and the market isolation that rural artisans experience. The platform enhances inter and intra‐group communication that supports rural artisans to establish their independent credibility in their trade without the assistance of intermediaries or middlemen.

Modified Technique to Rehabilitate American Society of Anesthesiology-III Patient, Through Flapless Procedure With Computed Tomography-Guided Surgery.

The aim was to evaluate the safety and effectiveness of a computed tomography-guided surgery implant placement with a flapless technique and immediate functional loading in an American Society of Anesthesiology-III patient. This technical note involved a 64-year-old American Society of Anesthesiology-III patient. Her hopeless teeth were extracted and a restorative evaluation was provided as a prosthetic reference. The surgical procedure was based on the flapless technique that let us to use local anesthesia. The authors used an All-on-4 concept restoration for maxilla and conventional fixed prosthesis procedures for jaw rehabilitation. The authors placed 4 tilted implants in the upper maxilla and 6 right implants in the jaw. Implants were loaded with a provisional prosthesis on the same day of surgery. Five months later, provisional restoration was removed; the authors placed into the ceramic crowns 2 Procera Implant Bridge (Nobel Biocare) frameworks, developed through computer-aided design/computer-aided manufacturing technology. Computed tomography-guided surgery is a minimally invasive technique that allows, through a flapless approach, safer and more predictable procedures. In this technical note, the authors achieved accurate implant placement and precise fit of restoration with natural looking appearance; this patient-oriented-treatment led to a reduced healing time with better compliance.