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

Optimising synthetic datasets for machine learning-based prediction of building damage due to tunnelling

Assessment of tunnelling-induced building damage is a complex Soil-Structure Interaction (SSI) problem, influenced by numerous geometric and material parameters of both the soil and structures, and is characterised by strong non-linear behaviour. Currently, there is a trend towards developing data-driven models using Machine Learning (ML) to capture this complex behaviour. Given the scarcity of real data, which typically comes from specific case studies, many researchers have turned to creating extensive synthetic datasets via sophisticated and validated numerical models like Finite Element Method (FEM). However, the development of these datasets and the training of advanced ML algorithms present significant challenges. poses significant challenges. Reliance solely on parameter domains and ranges derived from case studies can lead to imbalanced data distributions and subsequently poor performance of models in less populated regions. In this paper, we introduce a strategy for designing optimal high-confidence datasets through an iterative procedure. This process begins with a systematic literature review to determine the importance of parameters, their ranges, and dependencies as they pertain to building damage induced by SSI. Starting with several hundred FEM simulations, we generate an initial dataset and assess its quality and impact through Sensitivity Analysis (SA) studies, statistical modelling, and re-sampling in statistically significant regions. This evaluation allows us to refine the model’s input space, seeking scenarios that mitigate output distribution imbalances. The procedure is repeated until the datasets achieve a satisfactory balance for training metamodels, minimising bias effectively. Our findings highlight the success of this approach in identifying an optimal and feasible input space that significantly reduces imbalanced distributions of output features. This approach not only proves effective in our study but also offers a versatile methodology that could be adapted to other disciplines aiming to generate high-quality synthetic datasets

Parametric Design and Isogeometric Analysis of Tunnel Linings within the Building Information Modelling Framework

Both planning and design phase of large infrastructural project require analysis, modelling, visualization, and numerical analysis. To perform these tasks, different tools such as Building Information Modelling (BIM) and numerical analysis software are commonly employed. However, in current engineering practice, there are no systematic solutions for the exchange between design and analysis models, and these tasks usually involve manual and error-prone model generation, setup and update. In this paper, focussing on tunnelling engineering, we demonstrate a systematic and versatile approach to efficiently generate a tunnel design and analyse the lining in different practical scenarios. To this end, a BIM-based approach is developed, which connects a user-friendly industry-standard BIM software with effective simulation tools for high-performance computing. A fully automatized design-through-analysis workflow solution for segmented tunnel lining is developed based on a fully parametric design model and an isogeometric analysis software, connected through an interface implemented with a Revit plugin

Model update and real-time steering of tunnel boring machines using simulation-based meta models

A method for the simulation supported steering of the mechanized tunneling process in real time during construction is proposed. To enable real-time predictions of tunneling induced surface settlements, meta models trained a priori from a comprehensive process-oriented computational simulation model for mechanized tunneling for a certain project section of interest are introduced. For the generation of the meta models, Artificial Neural Networks (ANN) are employed in conjunction with Particle Swarm Optimization (PSO) for the model update according to monitoring data obtained during construction and for the optimization of machine parameters to keep surface settlements below a given tolerance. To provide a rich data base for the training of the meta model, the finite element simulation model for tunneling is integrated within an automatic data generator for setting up, running and postprocessing the numerical simulations for a prescribed range of parameters. Using the PSO-ANN for the inverse analysis, i.e. identification of model parameters according to monitoring results obtained during tunnel advance, allows the update of the model to the actual geological conditions in real time. The same ANN in conjunction with the PSO is also used for the determination of optimal steering parameters based on target values for settlements in the forthcoming excavation steps. The paper shows the performance of the proposed simulation-based model update and computational steering procedure by means of a prototype application to a straight tunnel advance in a non-homogeneous soil with two soil layers separated by an inclined boundary

Computationally efficient simulation in urban mechanised tunnelling based on multi-level BIM models

The design of complex underground infrastructure projects involves various empirical, analytical or numerical models with different levels of complexity. The use of simulation models in current state-of-the-art tunnel design process can be cumbersome when significant manual, time-consuming preparation, analysis and excessive computing resources are required. This paper addresses the challenges connected with minimising the user workload and computational time, as well as enabling real-time computations during the construction. To ensure a seamless workflow during design and to minimise the computation time of the analysis, we propose a novel concept for BIM-based numerical simulations, enabling the modelling of the tunnel advance on different levels of detail in terms of geometrical representation, material modelling and modelling of the advancement process. To ensure computational efficiency, the simulation software has been developed with special emphasis on efficient implementation, including parallelisation strategies on shared and distributed memory systems. For real-time on-demand calculations, simulation based meta models are integrated into the software platform. The components of the BIM-based multi-level simulation concept are described and evaluated in detail by means of representative numerical examples

Simulation based evaluation of time-variant loadings acting on tunnel linings during mechanized tunnel construction

In the design of machine driven tunnels, the loadings acting on the segmental lining are often adopted according to simplified assumptions, which improperly reflect the actual loading on the linings developing during the construction of a bored tunnel. A coupled 3D Finite Element model of the tunnel advancement process including the ring-wise installation of the lining and the hardening process of the grouting material serves as the basis for the analysis of the actual spatiotemporal evolution of the loading on the lining during tunnel construction. The distribution of the loadings in the different construction phases is calculated using a modified surface-to-surface contact condition imposed between the solidifying grouting material in the tail gap and the lining elements. An extensive parametric study investigates the influence of the initial grouting pressure, the pressure gradient, the temporal stiffness evolution, the soil permeability as well as the interface conditions between the grouting material and the tunnel shell on the temporal evolution of the loading on linings

Fusion of experimental and synthetic data for reliable prediction of steel connection behaviour using machine learning

The development of robust prediction tools based on machine learning (ML) techniques requires the availability of complete, consistent, accurate, and numerous datasets. The application of ML in structural engineering has been limited since, although real size experiments provide complete and accurate data, they are time-consuming and expensive. On the other hand, validated finite element (FE) models provide consistent and numerous synthetic data. Depending on the complexity of the problem, they might require large computational time and cost, and could be subjected to uncertainties and limitation in prediction capability given they are approximations of real-world problems. Hybrid approaches to combine experimental and synthetic datasets have emerged as an alternative to improve the reliability of ML model predictions. In this paper, we explore two hybrid methods to propose a robust approach for the prediction of the extended hollo-bolt (EHB) connection strength, stiffness, and column face displacement: (1) supervised ML methods with data fusion (DF) where learning is optimized with particle swarm optimization (PSO), and (2) artificial neural networks (ANN) based method with model fusion (MF). Based on the analysis of a dataset that combines 22 tensile experimental results with 2000 synthetic datapoints based on FE models, we concluded that using the first method (ML with DF and PSO) is the most suitable method for the prediction of the connection behavior. The ANN-based method with MF shows to be a promising method for the characterization of the EHB connection, however, more extensive experimental data is required for its implementation. Finally, a graphical user interface application was developed and shared in a public repository for the implementation of the proposed hybrid model

A review and analysis of testing and modeling practice of extended Hollo-Bolt blind bolt connections

Steel Hollow Sections (SHS) offer many structural, economical and architectural advantages in multi-storey and high-rise construction. However, their use is not suitable for a wide range of applications due to the difficulties of site bolting as there is limited access to the inner part of the steel section for tightening of standard bolts. Blind bolts have been developed to overcome these difficulties in view of extending the application of SHS in construction. Special attention has been paid to blind bolts that could potentially be used in rigid or semi-rigid connections. This is the case of a modified blind bolt, termed the Extended Hollo-Bolt (EHB), which has shown to be able to achieve the required performance for its use in moment resisting connections. This paper critically reviews published work concerning the blind fastener, describes the loading procedures used for testing and failure modes produced, lists the assessed parameters with their respective applicability ranges, and summarises the analytical models developed for the EHB components. Additionally, a global sensitivity analysis is performed using information of two representative studies for the purpose of detecting key design parameters that influence the response of the connection in terms of strength and stiffness. The analysis shows that the concrete strength has the most influential effect on both the stiffness and strength of the column component as well as bolt component stiffness, while the bolt grade highly influences the bolt component strength