Some open issues in the seismic design of bridges to Eurocode 8-2

This paper summarises the ongoing research on the seismic design of isolated and integral bridges at the University of Surrey. The first part of the paper focuses on the tensile stresses of elastomeric bearings that might be developed under seismic excitations, due to the rotations of the pier cap. The problem is described analytically and a multi-level performance criterion is proposed to limit the tensile stresses on the isolators. The second part of the paper sheds light on the response of integral bridges and the interaction with the backfill soil. A method for the estimation of the equivalent damping ratio of short-span integral bridges is presented to enable the seismic design of short period bridges based on Eurocode 8-2. For long-span integral bridges, a novel isolation scheme is proposed for the abutment. The isolator is a compressible inclusion comprises tyre derived aggregates (TDA) and is placed between the abutment and a mechanically stabilised backfill. The analysis of the isolated abutment showed that the compressible inclusion achieves to decouple the response of the bridge from the backfill. The analyses showed that both the pressures on the abutment and the settlements of the backfill soil were significantly reduced under the thermal and the seismic movements of the abutment. Thus, the proposed decoupling of the bridge from the abutment enables designs of long-span integral bridges based on ductility and reduces both construction and maintenance costs

Resilience Framework for Aged Bridges Subjected to Human-Induced Hazard - Case Study in Ukraine

Bridge structures are key components of transport networks, enabling connections between important centres and regions of countries. Their operability and functionality loss due to long-term deterioration or extreme hazards could cause crucial social and economic impacts. Assessment of bridge resilience against these hazards is needed to predict functionality, optimal management, sustainable development, and decision-making in maintenance and post-conflict restoration measures. Nevertheless, no studies exist to date to optimize resilience metrics for aged bridges subjected to human-induced stressors, considering indirect losses due to disruption of the transport network. This is a capability gap that gave the motivation for this research paper. The study covers functionality-related resilience metrics of damaged bridges, associated with direct losses in terms of repair cost, and socio-economic metrics due to the inoperability of the logistic route. The application of a framework for resilience assessment was illustrated with an example of the case study of the post-conflict restoration of Ukrainian aged bridge structures, which experienced extensive war-induced destruction. This research presents a novel application of resilience framework for assets, subjected to war-induced stressors, considering both direct and indirect losses, and introduces cost and safety-based resilience indexes

Damage characterisation using Sentinel-1 images:Case study of bridges in Ukraine

Bridges are vital infrastructure assets, ensuring the economic activity during the adverse times of conflict. Notwithstanding, there is insignificant research regarding their damage characterization with the use of remote approaches for post-conflict recovery. Monitoring and remote sensing is a promising technology for identification of damages caused by war-induced hazards, including artillery fire, explosions and shelling, and hence facilitate accurate and rapid evaluations of capacity and functionality loss, providing valuable information for reliable risk assessments at emergency and normal circumstances. The geospatial analysis, based on Interferometric SAR (InSAR) products of coherence, calculated between SAR images recorded at different dates could serve as a mean to characterize the level of damage, as demonstrated in this research. The main findings of study include the use fully open-access and remote data for assessment of critical infrastructure damages

Machine learning for predicting energy efficiency of buildings: a small data approach

This paper provides a method for predicting the energy efficiency of buildings using artificial intelligence tools. The scopes is twofold: prediction of the levels of the heating load and cooling load of buildings. A feature of this research is the performance of intellectual analysis in conditions of a limited amount of data when solving the stated tasks. An improved method of augmentation and prediction (input-doubling method) is proposed by processing data within each cluster of the studied dataset. The selection of the latter occurs due to the use of the fast and easy-to-implement k-means method. Next, a prediction is made using the input-doubling method within each separate cluster. The simulation of the method was performed on a real-world dataset of 768 observations. The proposed approach was found to have a high prediction accuracy in the absence of overfitting and high generalization properties of the improved method. Comparison with existing methods showed an increase in accuracy by 40-46% (MSE) compared to SVR with rbf kernel, which is the basis for the improved method, and by 5-12% (MSE) compared to the closest existing hierarchical predictor

Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies -- a tiered approach

Critical infrastructure, such as transport networks and bridges, are systematically targeted during wars and suffer damage during extensive natural disasters because it is vital for enabling connectivity and transportation of people and goods, and hence, underpins national and international economic growth. Mass destruction of transport assets, in conjunction with minimal or no accessibility in the wake of natural and anthropogenic disasters, prevents us from delivering rapid recovery and adaptation. As a result, systemic operability is drastically reduced, leading to low levels of resilience. Thus, there is a need for rapid assessment of its condition to allow for informed decision-making for restoration prioritisation. A solution to this challenge is to use technology that enables stand-off observations. Nevertheless, no methods exist for automated characterisation of damage at multiple scales, i.e. regional (e.g., network), asset (e.g., bridges), and structural (e.g., road pavement) scales. We propose a methodology based on an integrated, multi-scale tiered approach to fill this capability gap. In doing so, we demonstrate how automated damage characterisation can be enabled by fit-for-purpose digital technologies. Next, the methodology is applied and validated to a case study in Ukraine that includes 17 bridges, damaged by human targeted interventions. From regional to component scale, we deploy technology to integrate assessments using Sentinel-1 SAR images, crowdsourced information, and high-resolution images for deep learning to facilitate automatic damage detection and characterisation. For the first time, the interferometric coherence difference and semantic segmentation of images were deployed in a tiered multi-scale approach to improve the reliability of damage characterisations at different scales

Editorial. The crux in bridge and transport network resilience - advancements and future-proof solutions

Bridges and critical transport infrastructure (CTI) are primary infrastructure assets and systems that underpin human mobility and activities. Loss of the functionality of bridges has consequences on the entire transport network, which is also interconnected with other networks, therefore cascading events are expected in the entire system of systems, leading to significant economic losses, business, and societal disruption. Recent natural disasters revealed the vulnerabilities of bridges and CTI to diverse hazards (e.g. floods, blasts, earthquakes), some of which are exacerbated due to climate change. Therefore, the assessment of bridge and network vulnerabilities by quantifying their capacity and functionality loss and adaptation to new requirements and stressors is of paramount importance. In this paper, we try to understand what are the main compound hazards, stressors and threats that influence bridges with short- and long-term impacts on their structural capacity and functionality and the impact of bridge closures on the network operability. We also prioritise the main drivers of bridge restoration and reinstatement, e.g. its importance, structural, resources, organisational factors. The loss of performance, driven by the redundancy and robustness of the bridge, is the first step to be considered in the overall process of resilience quantification. Resourcefulness is the other main component of resilience here analysed

Invited perspectives : challenges and future directions in improving bridge flood resilience

Bridges are critical infrastructure components of road and rail transport networks. A large number of these critical assets cross or are adjacent to waterways and floodplains and are therefore exposed to flood actions such as scour, hydrodynamic loading and inundation, all of which are exacerbated by debris accumulations. These stressors are widely recognised as responsible for the vast majority of bridge failures around the world. While efforts have been made to increase the robustness of bridges to the flood hazard, many scientific and technical gaps remain. These gaps were explored during an expert workshop that took place in April 2021 with the participation of academics, consultants and decision makers operating in the United Kingdom and specialised in the fields of bridge risk assessment and management and floods. In particular, the following issues, established at different levels and scales of bridge flood resilience, were analysed: (i) characterization of the effects of floods on different bridge typologies, (ii) inaccuracy of formulae for scour depth assessment, (iii) evaluation of consequences of damage, (iv) recovery process after flood damage, (v) decision-making under uncertainty, and (vi) use of event forecasting and monitoring data for increasing the reliability of bridge flood risk estimations. These issues are discussed in this paper to inform other researchers and stakeholders worldwide, guide the directions of future research in the field, and influence policies for risk mitigation and rapid response to flood warnings, ultimately increasing bridge resilience