Search Results (31)

The integration of digital twin technology with structural health monitoring (SHM) is revolutionizing the assessment and maintenance of critical infrastructure, particularly bridges. Digital twins—virtual, data-driven replicas of physical structures—enable real-time monitoring by continuously synchronizing sensor data with computational models. This study presents the development of a real-time digital twin for a three-span steel railway bridge, utilizing a high-fidelity finite element (FE) model built using OpenSeesPy v 3.5 and instrumented with 18 strategically placed accelerometers. The dynamic properties of the bridge are extracted using Stochastic Subspace Identification (SSI), enabling an accurate estimation of modal parameters. To enhance the fidelity of the digital twin, a genetic algorithm-based model-updating strategy is implemented, optimizing the steel elastic modulus to minimize discrepancies between measured and simulated frequencies and mode shapes. The results demonstrate a remarkable reduction in frequency errors (below 5%) and a significant improvement in modal shape correlation (MAC > 0.93 post-calibration), confirming the model’s ability to reflect the bridge’s true condition. This work underscores the potential of digital twins in predictive maintenance, early damage detection, and life-cycle management of bridge infrastructure, offering a scalable framework for real-time SHM in complex structural systems. Full article

This article draws on over 40 years of the author’s experience with hydroinformatics tools for water and sustainability challenges, including flooding. It aims to spark discussion on urban flood risk and resilience rather than provide a literature review or definitive answers. Assessing urban flood risk and resilience is complex due to the spatio-temporal nature of rainfall, urban landscape features (e.g., buildings, roads, bridges and underpasses) and the interaction between aboveground and underground drainage systems. Flood simulation methods have evolved to analyse flood mitigation schemes, damage evaluation, flood risk mapping and green infrastructure impacts. Advances in terrain mapping technologies have improved flood analyses. Despite investments in flood management infrastructure, a residual flood risk remains, necessitating an understanding of the recovery and return to normality post-flood. Both risk and resilience approaches are essential for urban flood planning and management. Future challenges and opportunities include both technological and governance solutions, with artificial intelligence advancements offering the potential for digital twins to better protect urban communities and enhance the recovery from flood disasters. Full article

Ship–bridge collisions in inland waterways pose a serious threat to bridge infrastructure, often resulting in structural damage and jeopardizing safety. Despite the widespread deployment of collision warning systems, these systems fail to function effectively due to factors such as weather conditions, equipment malfunctions, and human error. Current alarming technologies, such as wavelet-based methods, are limited by poor real-time performance, high sensitivity to noise, and low localization accuracy, which hinder their practical application. This paper proposes an innovative Kalman filter–cointegration alarming (KFCA) technology, combining cointegration theory with Kalman filtering to achieve precise and real-time collision detection. Through numerical simulation, KFCA is validated, with the results summarized as follows: (i) KFCA effectively recognizes ship–bridge collisions under an SNR of 60, 70, and 80 dB; and (ii) it accurately identifies impact locations on the bridge based on sensor arrangement indices. Compared to existing methods, KFCA offers significant advantages in real-time response, noise resistance, and localization accuracy. This technology provides an efficient solution for bridge management departments, enabling the timely and accurate detection of ship–bridge collisions, thereby enhancing bridge safety and reducing secondary disasters. Full article

Uncertainties in traffic flows pose significant challenges for the accurate fatigue safety assessment of bridge structures. Fatigue analysis requires detailed information on heavy vehicle-induced loads, which can be obtained from weigh-in-motion (WIM) systems. This paper develops a stochastic traffic load model based on site-specific WIM measurements to evaluate the fatigue reliability of steel bridges by enhancing simulation efficiency and incorporating correlations in traffic load parameters. Traffic loading is measured on site by WIM systems and used to develop a probabilistic model. A heavy traffic scenario load model is developed based on the Gaussian mixture model (GMM) and Poisson distribution. The correlation between traffic load parameters is addressed using the Nataf transformation. The fatigue reliability of critical components is evaluated using this procedure as an illustrative example. The results show that annual increases in traffic load significantly impact fatigue damage. This research provides a theoretical basis for improved traffic management and structural maintenance strategies. Full article

In a hyperproductive interactive environment, where speed and cost-effectiveness often overshadow accuracy, the media’s role is increasingly shifting towards an educational function, beyond its traditional informative and entertaining roles. This shift, particularly through the promotion of science and education, aims to bridge the gap between educational institutions and the labor market. In this context, the importance of 21st-century competencies—encompassing a broad range of knowledge and skills—becomes increasingly clear. Educational institutions are now expected to equip students with relevant, universally applicable, and market-competitive competencies. This paper proposes using a combination of principal component analysis (PCA) and fuzzy analytic hierarchy process (FAHP) to rank 21st-century competencies developed throughout the educational process to improve the system. The highest-ranked competency identified is the ability to manage information—specifically, gathering and analyzing information from diverse sources. It has been shown that respondents who developed “soft skills” and media literacy during their studies are better able to critically assess content on social networks and distinguish between credible and false information. The significance of this work lies in its focus on the damaged credibility of online media caused by user-generated content and the rapid spread of unverified and fake news. Denying such discourse or erasing digital traces is therefore futile. Developing a critical approach to information is essential for consistently identifying fake news, doctored images, and recordings taken out of context, as well as preventing their spread. Full article

In recent years, active research has been conducted using deep learning to evaluate damage to aging bridges. However, this method is inappropriate for practical use because its performance deteriorates owing to numerous classifications, and it does not use photos of actual sites. To this end, this study used image data from an actual bridge management system as training data and employed a combined learning model for each member among various instance segmentation models, including YOLO, Mask R-CNN, and BlendMask. Meanwhile, techniques such as hyperparameter tuning are widely used to improve the accuracy of deep learning, and this study aimed to improve the accuracy of the existing model through this. The hyperparameters optimized in this study are DEPTH, learning rate (LR), and iterations (ITER) of the neural network. This technique can improve the accuracy by tuning only the hyperparameters while using the existing model for bridge damage identification as it is. As a result of the experiment, when DEPTH, LR, and ITER were set to the optimal values, mAP was improved by approximately 2.9% compared to the existing model. Full article

Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety of the railway infrastructure and uses multi-criteria analysis (MCA) alongside an analytical hierarchy process (AHP) to produce flood susceptibility maps within a geographic information system (GIS). The proposed methodology was applied to the catchment area of a railway track in southern Italy that was heavily affected by a destructive flood that occurred in the autumn of 2015. Two susceptibility maps were obtained, one based on static geophysical factors and another including triggering rainfall (dynamic). The results showed that large portions of the railway line are in a very highly susceptible zone. The flood susceptibility maps were found to be in good agreement with the post-disaster flood-induced infrastructural damage recorded along the railway, whilst the official inundation maps from competent authorities fail to supply information about flooding occurring along secondary tributaries and from direct rainfall. The reliable identification of sites susceptible to floods and damage may provide railway and environmental authorities with useful information for preparing disaster management action plans, risk analysis, and targeted infrastructure maintenance/monitoring programs, improving the resilience capacity of the railway network. The proposed approach may offer railway authorities a cost-effective strategy for rapidly screening flood susceptibility at regional/national levels and could also be applied to other types of linear transport infrastructures. Full article

Bridges are essential to infrastructure and transportation networks, but face challenges from heavier traffic, higher speeds, and modifications like busway integration, leading to potential overloading and costly maintenance. Structural Health Monitoring (SHM) plays a crucial role in assessing bridge conditions and predicting failures to maintain structural integrity. Vibration-based condition monitoring employs non-destructive, in situ sensing and analysis of system dynamics across time, frequency, or modal domains. This method detects changes indicative of damage or deterioration, offering a proactive approach to maintenance in civil engineering. Such monitoring systems hold promise for optimizing the management and upkeep of modern infrastructure, potentially reducing operational costs. This paper aims to assist newcomers, practitioners, and researchers in navigating various methodologies for damage identification using sensor data from real structures. It offers a comprehensive review of prevalent anomaly detection approaches, spanning from traditional techniques to cutting-edge methods. Additionally, it addresses challenges inherent in Vibration-Based Damage (VBD) SHM applications, including establishing damage thresholds, corrosion detection, and sensor drift. Full article

Road structures undergo a series of chemical and physical processes once they are put into service. This phenomenon results from the action of the load and the influence of the environment, which causes their progressive deterioration. In order to mitigate the risk of progressive deterioration and guarantee their stability and durability, various maintenance tasks are required, including visual inspections. The Intelligent Bridge Management System of Colombia (SIGP) includes visual inspection as one of its modules. The system has been designed based on state-of-the-art criteria and national experience with relevant damages and bridge collapses. This paper presents the visual inspection methodology, which includes several stages such as a classification scale, condition index, evaluation areas, damage catalog, and evaluation criteria. In addition, a digital application has been developed to facilitate real-time data collection during field inspections using mobile devices, which can be uploaded directly to the system database hosted in the cloud. The results from the inspection of bridges of different typologies and years of construction are presented, as well as general inspection results from 150 bridges in Colombia. The relevance, comprehensiveness, and accuracy of the inspection are supported by a damage catalog, which allows the identification of intervention needs and reduces the bias of the collected data. Full article

Currently, damage in aging bridges is assessed visually, leading to significant personnel, time, and cost expenditures. Moreover, the results depend on the subjective judgment of the inspector. Machine-learning-based approaches, such as deep learning, can solve these problems. In particular, instance-segmentation models have been used to identify different types of bridge damage. However, the value of deep-learning-based damage identification may be reduced by insufficient training data, class imbalance, and model-reliability issues. To overcome these limitations, this study utilized photographic data from real bridge-management systems for the inspection and assessment of bridges as the training dataset. Six types of damage were considered. Moreover, the performances of three representative deep learning models—Mask R-CNN, BlendMask, and SWIN—were compared in terms of loss–function values. SWIN showed the best performance, achieving a loss value of 0.000005 after 269,939 training iterations. This shows that bridge-damage-identification performance can be maximized by setting an appropriate learning rate and using a deep learning model with a minimal loss value. Full article

Neutrophils represent the primary defense against microbial threats playing a pivotal role in maintaining tissue homeostasis. This review examines the multifaceted involvement of neutrophils in periodontitis, a chronic inflammatory condition affecting the supporting structures of teeth summarizing the contribution of neutrophil dysfunction in periodontitis and periodontal-related comorbidities. Periodontitis, a pathological condition promoted by dysbiosis of the oral microbiota, is characterized by the chronic inflammation of the gingiva and subsequent tissue destruction. Neutrophils are among the first immune cells recruited to the site of infection, releasing antimicrobial peptides, enzymes, and reactive oxygen species to eliminate pathogens. The persistent inflammatory state in periodontitis can lead to aberrant neutrophil activation and a sustained release of proinflammatory mediators, finally resulting in tissue damage, bone resorption, and disease progression. Growing evidence now points to the correlation between periodontitis and systemic comorbidities. Indeed, the release of inflammatory mediators, immune complexes, and oxidative stress by neutrophils, bridge the gap between local and systemic immunity, thus highlighting neutrophils as key players in linking periodontal inflammation to chronic conditions, including cardiovascular diseases, diabetes mellitus, and rheumatoid arthritis. This review underscores the crucial role of neutrophils in the pathogenesis of periodontitis and the complex link between neutrophil dysfunction, local inflammation, and systemic comorbidities. A comprehensive understanding of neutrophil contribution to periodontitis development and their impact on periodontal comorbidities holds significant implications for the management of oral health. Furthermore, it highlights the need for the development of novel approaches aimed at limiting the persistent recruitment and activation of neutrophils, also reducing the impact of periodontal inflammation on broader health contexts, offering promising avenues for improved disease management and patient care. Full article

Rapid urban development and increase in construction have significantly altered the surface coverage of cities, resulting in a rise in impervious surfaces such as roofs, streets, and pavements. These changes act as barriers against rainwater infiltration into the soil, leading to a substantial increase in surface runoff. Managing surface runoff has become a critical task in civil engineering and urban planning, as it can mitigate damage and provide opportunities for utilizing excess water. However, traditional flood control and guidance systems tend to be extensive and expensive, prompting researchers to explore cost-effective alternatives that consider all design parameters and variables. In this research, we propose an innovative approach that combines the NSDE (non-dominated sorting differential evolution) metaheuristic algorithm as an optimizer with the SWMM (storm water management model) as a simulator. The objective is to design efficient surface runoff collection networks by thoroughly investigating their hydraulic behaviors. This study focuses on the Chitgar watershed in Tehran, Iran, utilizing the SWMM model and NSDE multi-objective metaheuristic algorithm to determine the optimal dimensions of the channel and its intersecting structures. The aim is to minimize costs and reduce water leakage from the network. A comparison is made between the optimized design results and the existing network plan (without any design modifications). The analysis reveals substantial reductions in water leakage for all three design scenarios: a 7.66% reduction when considering only bridges, a 7.35% reduction with only the canal, and an impressive 95.26% reduction when both the canal and bridges are incorporated. These findings demonstrate the superiority of the optimized designs in terms of cost-effectiveness and the efficient management of surface runoff. Full article

The Italian Ministry of Infrastructure and Transportation adopted the guidelines on risk classification and management, safety assessment and monitoring of existing bridges through the Decree No. 578 dated 17 December 2020. This document must be used by all managing entities to prevent damage due to a lack of maintenance to these crucial components of the infrastructure system. The approach of the guidelines for existing bridges is developed across six levels, ranging from Level 0 to Level 5. The research work presented in this article is focused on Level 3, which pertains to preliminary assessments conducted on existing bridges. Through an automated procedure, the preliminary verification is performed by comparing bending and shear stress generated by traffic load schemes extracted from previous standards with the ones based on the current code. These loads are applied to a series of girder deck models, selected through a statistical study conducted on a database of bridges. Performance indices are derived from the comparison to evaluate the adequacy of previously designed and constructed structures by applying the load models specified in the current regulations for designing new bridges. The analysis results highlight a performance gap, which varies depending on the standard code at hand. Full article

Damage to bridge expansion joints arises from a variety of causes such as increasingly deteriorated bridges, abnormal temperatures, and increased traffic. To detect anomalies in the expansion joints, this study proposes an Artificial Intelligence (AI)-model-based diagnosis method of analyzing the vibration of the bridge bearing that supports the upper structure of a bridge. The proposed system establishes big data with the measured displacement of a bridge bearing and makes an AI-based prediction about the risk of bridge expansion joints. Replacing a bridge bearing makes it possible to manage the bridge displacement before and after construction and helps improve safety inspections and diagnosis methods. It is necessary to prepare a bridge with anomalies for the AI model training. For this reason, a bridge with a bridge bearing was simulated. In addition, a vehicle suitable for the bridge was simulated. The displacement data in normal and abnormal situations were collected, cleaned, and applied to the AI analysis model. The system was found to have over 90% accuracy of prediction about expansion joint faulting and damage. Full article

Railway infrastructures play a pivotal role in developing the national transportation system. Recently, the strategy of the railway engineer has been significantly shifted; along with the development of new assets, they tend to pay increasing attention to the operation and management of existing railway assets. In this regard, this paper proposes a Digital Twin (DT) model to improve fatigue assessment efficiency in the operational processes of railway steel bridges (RSBs). The DT concept mainly lies in the federation and interaction of a Fatigue Analysis System (FAS), which is based on Eurocodes principles, and a model in Building Information Modeling (BIM). Along with the proposed DT concept, a prototyping system for a real bridge is initiated and curated. The FAS is validated in good-agreement results with the ambient vibration test of the bridge (about 1.6% variation between numerical and experimental values), and close values were found between numerical and experimental stresses, the latter obtained by installing strain gauges on the bridge. The BIM model provides access to the numerical values of fatigue state results in a given bridge connection detail but also automatically represents that information in a 3D environment using a color-scale-based visualization process. Furthermore, a simulation model with the main input variables being the traffic and geometric conditions of the bridge is continuously updated for timely re-evaluation of the damage state, which shows promise for the lifecycle management of the bridge. Full article