Different Levels Of Detail Research Articles

Optical Networking Gym: an open-source toolkit for resource assignment problems in optical networks

The dynamic provisioning of optical network services requires algorithms to find a suitable solution given the specific service requirements and the current network state. These algorithms are usually evaluated using a software simulator developed ad hoc, which may require different levels of detail depending on the problem addressed and how realistic the evaluation needs to be. Moreover, to demonstrate they are a significant contribution to the field, these new algorithms must be benchmarked against the best-performing previously proposed solutions. Due to the large set of parameters and their wide range of possible values, benchmarking algorithms from the literature is not straightforward and can quickly become challenging and time-consuming. This work introduces the Optical Networking Gym, an open-source toolkit that simplifies implementing optical resource assignment simulations and benchmarking new solutions against previously published algorithms. The toolkit provides environments modeling relevant optical networking scenarios, common algorithms for solving problems related to these scenarios, and a set of scripts to prepare and execute simulations for various use cases. Currently, four environments are available, with the possibility of increasing this number through contributions from the co-authors and the community. This paper describes the architecture, interface, environments, and scripts included with the toolkit. We adopt the quality of transmission (QoT)-aware dynamic resource allocation of optical services as the network scenario under examination. Three use cases highlight the toolkit’s modularity, flexibility, and performance. The toolkit allows researchers to streamline the process of developing simulation scenarios and algorithms, enhancing their ability to benchmark their algorithms.

The method of forming ensembles of complex signals based on multi-scale decomposition of time intervals at different levels of detail

This article addresses the challenges of forming signal ensembles in dynamic cognitive radio environments, focusing on the limitations of traditional methods. The study proposes a new approach based on multiscale time interval decomposition, which allows for the creation of signal ensembles at varying levels of temporal detail. The key innovation of this method is its ability to improve signal reproduction accuracy, reduce inter-symbol and inter-channel interference, and enhance the overall efficiency of data processing. The method's adaptability to changing environmental conditions is also a core advantage, enabling better use of bandwidth and reduced transmission delay. Through the decomposition of time intervals into coarse, intermediate, and fine levels, this method allows for the detailed analysis of short-term, medium-term, and long-term signal components. Experimental results demonstrate the superiority of this approach in terms of signal recovery accuracy, noise resilience, and processing speed. These findings highlight the potential for optimizing signal processing in cognitive radio networks, particularly in environments with high levels of noise and interference. Future research aims to integrate machine learning algorithms to further enhance adaptability in real-time scenarios.

Latent fingerprint detection and identification using yellow-emitting YOF:Pr3+ nanophosphor

In this study, YOF:Pr3+ nanophosphors were synthesized using the microwave-assisted hydrothermal route. The structural and morphological properties of the nanophosphors calcined at different temperatures were studied in detail to optimize the synthesis conditions. The visible region and near-infrared (NIR) photoluminescence (PL) properties of the nanophosphors were obtained. Intense emission bands were observed in the blue and red regions under 250 nm excitation. The CIE chromaticity diagram showed that the color coordinates of the nanophosphors were located in the yellow region. The optimum Pr3+-concentration was found to be 0.002 mol fraction. The application of YOF: 0.002 Pr3+ nanophosphor for latent fingerprint detection was examined and evaluated on different types of surfaces. The nanophosphor demonstrated excellent adherence to the ridge patterns and sufficient sensitivity required for distinguishing between the ridges and the furrows. Different levels of detail were also observed that provided necessary information for the individualization of the fingerprints. The results suggest that the prepared YOF:Pr3+ nanophosphor can be a potential candidate for the rapid visualization and detection of latent fingerprints.

Ethogram of the predatory sequence of dogs (Canis familiaris)

Predatory behaviour is a relevant part of the behavioural repertoire of dogs, which often entails negative or desirable implications, depending on the context and target to which it is directed. It is also considered one of the most characterising aspects of breed-specific behaviour. Despite its relevance, there is surprisingly little scientific knowledge about the topic. In the present study we sought to develop an ethogram of the predatory sequence of dogs, with the aim of providing a sufficiently general structure to be applicable to dogs at large, without eluding peculiar aspects of the species’ specific behaviour. Sixty videos of dogs of different breeds expressing predatory acts, including both videos obtained from public internet repositories and videos made on purpose, were described in detail by two different observers. From descriptions, a general sequence was identified, consisting of four functional phases, i.e. search, approach, chase and bite. The descriptions of the behaviours included in each phase were organised into three levels, characterised by different levels of detail and focusing on different aspects of the behaviour. When appropriate, these descriptions highlighted differences in behaviour expressed by dogs belonging to different working groups. The ethogram proposed in the present paper could represent a useful starting point for improving knowledge about the predatory behaviour of dogs and answering relevant questions about both its evolution and ontogeny.

Spatial Analysis with Detailed Indoor Building Models for Emergency Services

This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation scenario. Indoor building models of a complicated train station with different levels of detail are generated from two-dimensional (2D) floor plans and Building Information Model (BIM) datasets. In addition to the 3D building models, spatial and non-spatial attributes are also associated with the created building models and the objects within them. The ant colony optimization (ACO) algorithm is modified to analyze the indoor building models for emergency service decision support applications. The detailed indoor models and the proposed spatial analysis algorithms are tested in simulated emergency evacuation scenarios to select the best routes during emergency services. The experimental results demonstrate that the proposed system is helpful for selecting the optimal route with the least cost at varying time stamps. Together with the developed spatial analysis framework, they have a great potential for effective decision support during emergency situations.

Exploring the landscape of system dynamics archetypes: A systematic review

AbstractSystem dynamic archetypes reflect common problematic behaviours that occur in the real world. The high level of abstraction embodied in these archetypes is both promising and daunting. Identifying relevant archetypes for real‐world problems is not a straightforward task. In this work, we aim to provide a comprehensive background for understanding current knowledge on the application of system archetypes. The methodology is based on a systematic literature review. Most of the works contribute with prescriptive models that address problems in specific areas. These models generally tackle complex problems where social, environmental and political issues converge. The unit of analysis encompasses geographical units, organizations and artefacts. The steps followed in the development are described with different level of detail. Stakeholders' participation is typically integrated into the development process. Some reported problems are that archetypes could induce a hypothesis that does not include all relevant factors; some participants may have difficulty in understanding the models; confidence in results, difficulty of having data to configure all the variables; and the cost burden for small enterprises.

Numerical analysis of laminar velocity-forced premixed slit flames using modal decomposition techniques

The relation between Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD) and Flame Transfer Functions (FTF) is explored to gain further insight into the dynamics of two-dimensional laminar slit flames externally forced by velocity perturbations. The application of POD to the heat release rate fields suggests that the resultant modes can be split into two groups: the ones that are related to the displacement of the reactive sheet in the normal direction with regards to the flame front, and the ones that contain the local flame front distortions. The latter modes show preferential frequencies associated to the phase values of π, 2π and 3π of the FTFs, which can be related to the maximum gain value depending on the case. Furthermore, the results of the modal analysis seem to support that the flame tip dynamics can be conceptually modelled as a set of standing waves whose joint response can reconstruct a propagation in the normal flame front direction, generating also the temporal fluctuations of the spatially-integrated heat release in the domain. The DMD analysis shows the existence of an interaction between the flame and the flow, and illustrates the fundamental role played by the velocity perturbations at the base for the motion of the reactive sheet. Thus, the analysis shows how data-based decomposition methods can be used to identify complex physical phenomena contained in the FTF graphs with different levels of detail, and extend the modal analysis to the physical space.Novelty and significance statementThis paper proves the potential of modal decomposition techniques like POD and DMD to infer the underlying physics of canonical flames and their relation to the FTF, validating also the hypotheses and methodology of other reduced-order models in the literature. A canonical configuration featuring a two-dimensional slit flame has been selected to analyse the flame dynamics using these methods. Hence, this work aims to set the foundations to elaborate data-driven reduced-order models based on modal decomposition techniques to support the analysis and the study of flame dynamics.

An object-oriented implementation of a recursive “quantum network” solver and its application to district heating networks

With the increasing importance of energy efficiency and sustainability, the demand for high-performance district heating networks is also on the rise. As traditional engineering methods were often no longer sufficient, several packages for numerical simulation have evolved. Many of them offer high accuracy at the cost of considerable manual preparation and computational effort. However, there is still no user-friendly software that is equally suitable for simplified studies in the early project phase, for the rapid optimisation of multiple concepts and for subsequent detailed planning and dimensioning. For this reason and in cooperation with some companies from the energy sector, we had conceived a novel, highly flexible modular approach, the so-called “quantum networks”, where all parts of the district heating network are appropriately abstracted into quantum elements. Now we present our recent implementation of this model in an object-oriented C++ library. Starting from a generalised base class and making use of the concepts of inheritance and polymorphism, the idea of different levels of detail for the same element type is directly realised and can always be further refined. In addition, as all elements are derived from the same base class, they all share the same outer appearance and can thus be easily combined or interchanged. Based on these prerequisites, a dedicated thermo-hydraulic solver has then been developed. Thanks to its recursive design requiring neither outer iterations nor matrix inversions, it proves to be extremely fast and is therefore suitable for rapid design or optimisation studies in which a vast number of configurations has to be computed. To conclude this part of the work, the developed C++ library was benchmarked on two use cases covering a full year of operation that can now be computed in approximately one second of runtime.

The role of agroforestry in solving the agricultural landscapes vulnerabilities in the Po Plain district

AbstractThe current trend of biodiversity deterioration in rural systems is a complex issue that operates across multiple spatial scales. Agroforestry practices have the potential to positively contribute towards addressing these trends by shaping the structure of agricultural landscapes and their underlying ecological functions. This study aims to test a multi-scale analytical approach to understand and account for these processes. Specifically, the study seeks to assess the contributions that agroforestry practices at the farm scale can make towards supporting biodiversity, in response to the wider-scale landscape eco-mosaic structural and functional challenges and requirements (both at the local and extra-local landscape systems). To achieve this, a series of landscape ecology analyses are conducted on an agroforestry-based rice farm located in the western Po Plain region of Northern Italy. These analyses examine various landscape structural traits (such as matrix composition, patch size, shape complexity, and diversity indices) and functional traits (including connectivity and bionomic indices), with different levels of detail for each scale of analysis. This allows for the evaluation of the current ecological status of both the extra-local and local scale landscape systems (including drivers of vulnerability and resilience) and the assessment of the farm's current contributions to biodiversity support. Based on these findings, strategic agroforestry interventions are identified at the farm scale to enhance its capacity to address the wider-scale ecological gaps. Two design scenarios are assessed, wherein functional ecological traits such as landscape diversity, connectivity, and ecological stability are improved. The results confirm the role of farm scale agroforestry management as a buffering tool, demonstrating how it contributes to the restoration of broader-scale landscape vulnerabilities. The applied approach provides cost-effective assessments of biodiversity-related ecological processes, with the accuracy of the findings dependent on the comprehensive multi-scale analysis conducted.

The importance of model structure and soil data detail on the simulations of crop growth and water use: A case study for sugarcane

Process-based crop models have faced rapid development over the last years, and many modelling platforms are now available and can be used in a wide range of conditions. Whilst the selection of a model should be suited to the purpose of its application, very few studies focused on the impact of choosing different model structures and data details on the simulation outputs. One important aspect is the soil water dynamics, which can be simulated at different levels of details in terms of data and approaches. In this study, we investigated the impact of model structure and data detail on simulations of sugarcane growth and irrigation scheduling. Three different soil water routines (Standalone, Tipping-Bucket, SWAP) were coupled with the SAMUCA model and calibrated with a comprehensive field experiment dataset. We also tested the influence of using simplified homogeneous (SL) and detailed (DL) soil profile information in model performance. The model framework was evaluated against independent field experiments across Brazil and used to simulate long-term sugarcane growth and irrigation scheduling. After calibration, the SWAP-DL showed the highest accuracy in soil moisture predictions, with a 6 % error (RRMSE), but the difference from TippingBucket-DL was small (8 %). While the performance of stalk dry mass, LAI and water-use efficiency simulations were within the range found in literature, comprehensive field experiments showing significant impacts of drought on sugarcane growth are still lacking for a more rigorous evaluation. Both SWAP and tipping-bucket approaches showed higher robustness to soil data detail as compared to the Standalone method, which should be avoided when soil water is critical for sugarcane growth. The use of tipping-bucket method may still be preferred when the research goal is focused on crop growth and soil parameters are limited. SWAP-SAMUCA may provide an extended ability to represent agrohydrological processes in sugarcane plantations and process understanding.

Энергосбережение и оценка энергоэффективности угледобывающего предприятия

The article is devoted to consideration of issues related to assessing the energy efficiency of a coal mining enterprise. Coal mining enterprises are characterized by a large volume of energy intensive ore mining operations. The article presents possible ways of energy saving in the mining industry. During the research, a system of energy efficiency indicators for coal mining enterprises is presented. Materials and methods. The article proposes a system of indicators for coal enterprises based on existing methods and approaches to assessing energy efficiency. Energy consumption dependence graphs, benchmarking, and pinch analysis are considered as a starting point. Results. The study presents a system of energy efficiency indicators for coal mining enterprises. The proposed system has several levels. The existing methods and approaches for assessing this important indicator for industrial enterprises in the coal industry as a whole are considered. The author’s approach to formalizing the system of energy efficiency indicators for coal mining enterprises is proposed. The significance of energy audit as a key assessment measure is indicated and its stages are highlighted. The influencing factors determining energy costs and their reduction for a coal mining enterprise are systematized, barriers preventing the practical implementation of energy saving measures are analyzed. Discussion. The proposed metrics system provides a comprehensive framework for assessing the energy efficiency of coal mining enterprises at various levels. Its flexibility allows it to be applied to the analysis of individual equipment units, production processes, and overall enterprise performance. Its multi-level approach provides a detailed understanding of energy consumption, which allows for informed decision-making to optimize operations. At the equipment level, the metrics system evaluates the energy consumption and efficiency of individual machines and devices. At the production process level, the metrics system analyzes the energy consumption and efficiency of various stages of coal mining. At the enterprise level, the metrics system provides a comprehensive assessment of overall energy efficiency. It takes into account energy consumption at all levels and allows enterprises to compare their performance with each other and with industry benchmarks. Conclusion. Energy efficiency assessment of a coal mining enterprise allows to understand how and where energy is used, where its use can be improved, and how to reduce its costs. To conduct an energy efficiency assessment, the article proposes a system of indicators, highlighting the features of an energy audit. The main advantage of the proposed system of indicators is that it covers a wide range of energy consumption indicators, which are carefully selected and located at different levels of detail. In general, a balanced approach to the level of detail and generalization in the system of indicators provides flexibility in adapting indicators to specific research questions and helps to interpret the results in the context of structural differences between energy systems. Resume. Overall, the proposed metrics system offers a comprehensive approach to analyzing the energy efficiency of coal mining enterprises, covering all levels of operations. Its use allows enterprises to improve energy efficiency, reduce operating costs, and contribute to a more sustainable coal mining industry. Suggestions for practical applications and directions for future research. The use and implementation of the developed system at a coal mining company allows it to improve energy efficiency, reduce operating costs and contribute to a more sustainable coal mining industry.

CAN: Concept‐Aligned Neurons for Visual Comparison of Deep Neural Network Models

AbstractWe present concept‐aligned neurons, or CAN, a visualization design for comparing deep neural networks. The goal of CAN is to support users in understanding the similarities and differences between neural networks, with an emphasis on comparing neuron functionality across different models. To make this comparison intuitive, CAN uses concept‐based representations of neurons to visually align models in an interpretable manner. A key feature of CAN is the hierarchical organization of concepts, which permits users to relate sets of neurons at different levels of detail. CAN's visualization is designed to help compare the semantic coverage of neurons, as well as assess the distinctiveness, redundancy, and multi‐semantic alignment of neurons or groups of neurons, all at different concept granularity. We demonstrate the generality and effectiveness of CAN by comparing models trained on different datasets, neural networks with different architectures, and models trained for different objectives, e.g. adversarial robustness, and robustness to out‐of‐distribution data.

The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest-dwelling bats in Germany.

In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland-Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land-cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.

Automatically generated state observers – toolchain and validation with real measurements

State estimation of wind turbines is the basis for many advanced control strategies and analysis applications such as condition monitoring, fault diagnosis, and much more. It depends on the application and its goals, which states of the real turbine are relevant. Therefore, estimators with different levels of detail and sets of states suitable for the problem at hand are needed. This paper describes a toolchain that generates custom-tailored code for an extended Kalman filter. The aim is to create computationally highly efficient estimators for a wide range of applications. Automatic generation of source code allows for agile experimentation and reduces the risk of time-consuming implementation errors. The new contributions are the selection and combination of all parts of the toolchain, the extension of the integration algorithm to include the Jacobian of the transition function, and the adaptation of an efficient covariance auto-tuning algorithm. Four different models are validated in a three-stage process, including measurement data from a real turbine.

An open computational toolbox to analyze multi- and single-unit sympathetic nerve activity in microneurography.

Microelectrode recordings from human peripheral and cranial nerves provide a means to study both afferent and efferent axonal signals at different levels of detail, from multi- to single-unit activity. Their analysis can lead to advancements both in diagnostic and in the understanding of the genesis of neural disorders. However, most of the existing computational toolboxes for the analysis of microneurographic recordings are limited in scope or not open-source. Additionally, conventional burst-based metrics are not suited to analyze pathological conditions and are highly sensitive to distance of the microelectrode tip from the active axons. To address these challenges, we developed an open-source toolbox that offers advanced analysis capabilities for studying neuronal reflexes and physiological responses to peripheral nerve activity. Our toolbox leverages the observation of temporal sequences of action potentials within inherently cyclic signals, introducing innovative methods and indices to enhance analysis accuracy. Importantly, we have designed our computational toolbox to be accessible to novices in biomedical signal processing. This may include researchers and professionals in healthcare domains, such as clinical medicine, life sciences, and related fields. By prioritizing user-friendliness, our software application serves as a valuable resource for the scientific community, allowing to extract advanced metrics of neural activity in short time and evaluate their impact on other physiological variables in a consistent and standardized manner, with the final aim to widen the use of microneurography among researchers and clinicians.

Revealing the complexity of meniscus microvasculature through 3D visualization and analysis

Three-dimensional information is essential for a proper understanding of the healing potential of the menisci and their overall role in the knee joint. However, to date, the study of meniscal vascularity has relied primarily on two-dimensional imaging techniques. Here we present a method to elucidate the intricate 3D meniscal vascular network, revealing its spatial arrangement, connectivity and density. A polymerizing contrast agent was injected into the femoral artery of human cadaver legs, and the meniscal microvasculature was examined using micro-computed tomography at different levels of detail and resolution. The 3D vascular network was quantitatively assessed in a zone-base analysis using parameters such as diameter, length, tortuosity, and branching patterns. The results of this study revealed distinct vascular patterns within the meniscus, with the highest vascular volume found in the outer perimeniscal zone. Variations in vascular parameters were found between the different circumferential and radial meniscal zones. Moreover, through state-of-the-art 3D visualization using micro-CT, this study highlighted the importance of spatial resolution in accurately characterizing the vascular network. These findings, both from this study and from future research using this technique, improve our understanding of microvascular distribution, which may lead to improved therapeutic strategies.

Acceleration of computations in modelling of processes in complex objects and systems

The development of methods of parallelization of computing processes, which involve the decomposition of the computational domain, is an urgent task in the modeling of complex objects and systems. Complex objects and systems can contain a large number of elements and interactions. Decomposition allows you to break down a system into simpler subsystems, which simplifies the analysis and management of complexity. By dividing the calculation area of the part, it is possible to perform parallel calculations, which increases the efficiency of calculations and reduces simulation time. Domain decomposition makes it easy to scale the model to work with larger or more detailed systems. With the right choice of decomposition methods, the accuracy of the simulation can be improved, since different parts of the system may have different levels of detail and require appropriate methods of additional analysis. Decomposition allows the simulation to be distributed between different participants or devices, which is relevant for distributed systems or collaborative work on a project. In this work, mathematical models are built, which consist in the construction of iterative procedures for "stitching" several areas into a single whole. The models provide for different complexity of calculation domains, which makes it possible to perform different decomposition approaches, in particular, both overlapping and non-overlapping domain decomposition. The obtained mathematical models of subject domain decomposition can be applied to objects and systems that have different geometric complexity. Domain decomposition models that do not use overlap contain different iterative methods of "stitching" on a common boundary depending on the types of boundary conditions (a condition of the first kind is a Dirichlet condition, or a condition of the second year is a Neumann condition), and domain decomposition models with an overlap of two or more areas consist of the minimization problem for constructing the iterative condition of "stitching" areas. It should be noted that the obtained models will work effectively on all applied tasks that describe the dynamic behavior of objects and their systems, but the high degree of efficiency of one model may be lower than the corresponding the degree of effectiveness of another model, since each task is individual. Keywords: mathematical modelling, decomposition of the computational domain, parallelization, optimization, complex objects and systems.

Visualization of educational data in a German-language corpus of student texts

Introduction. Digital transformation of education involves the use of digital tools in managing the educational process as a whole and in the learning process in individual disciplines. The specifics of teaching a foreign language require special functionality from digital tools. The purpose of the article is to explore the possibilities of a corpus of student texts in a foreign language for analyzing the results of students’ educational work and forming an adapted learning path within the discipline related to the study of a foreign language. Materials and methods. The material for the study was the Petrozavodsk Annotated Corpus of Texts (PACT), containing written works in German and French written by students from 2019 to 2023. Texts are accompanied by attributive information about the author, writing conditions, evaluation and error corrections. A web application has been developed to work with the corpus. The application includes personal accounts for teachers and students. To analyze the state of the learning process and make subsequent decisions on managing the learning process, the web application contains several tools for visualizing error statistics at different levels of detail with the ability to select texts according to different conditions. Results of the study. As part of the study, patterns were identified that connect the types of errors with the genres of texts, with the severity of the errors, and with the emotional and physiological state of the student. Russian-speaking students studying German as a foreign language make the most mistakes in the choice of lexemes, spelling, punctuation and the place of the verb in a sentence. At the same time, genre differences can be observed to distribute the number of errors by their type. There is an increase in the relative number of gross errors by 1.5 times for senior students compared to the first year. Errors in the choice of lexeme are critical for understanding the content of the text. The severity of the error does not depend on the genre of the text. Conclusion. The results of the study can be used to build corpora of student texts with educational analytics functions in dashboard format. The results obtained can be useful in developing teaching materials for the “Foreign Language” discipline.

Sustainability of underground infrastructure – Part 1: Digitalisation-based carbon assessment and baseline for TBM tunnelling

Alongside the transition towards sustainable and renewable infrastructure and energy production, tunnelling projects that adopt tunnel boring machines (TBM) continue to increase in size and complexity. Life Cycle Assessment (LCA) methodologies have a significant impact on the Architecture, Engineering and Construction (AEC) industry, with a shift in focus to cost and carbon management across the infrastructure lifecycle through the use of digitalisation techniques and sustainability rating systems. This paper aims to develop a systematic framework to enhance the sustainability of underground infrastructure in the early design and construction stages, aiming for “build clever and efficiently”. This framework integrates digitalisation, carbon assessment standards, numerical modelling, and optimisation techniques to assess carbon emissions and establish benchmarks for construction products and processes. The approach uses parametric models to define tunnelling-related entities such as geological formations, TBM, and segments, designed with dimensional constraints that allow for adaptive modelling and interactions. Enabled by building information modelling (BIM) through parametric models enriched with domain properties, module-based carbon accounting is adopted to evaluate the footprint of material- and asset-level key emitters with functional units. Leveraging established databases, baselines for segmental lining designs and carbon reduction strategies are established. Through project-driven case studies, the correlations among geotechnical conditions, tunnel dimensions, and TBM operating parameters are developed to quantify TBM carbon emissions. A prototype programme demonstrates the framework's application on typical metro and road tunnels at different levels of details (LoD). This paper serves as a guideline for conducting embodied carbon assessments specifically tailored to TBM tunnelling, acknowledging the substantial contribution of the TBM construction process, which is controlled by geotechnical conditions.

Methodology for the Integration of Structural Health Assessment of Masonry Bridges into HBIM

ABSTRACT A methodology is proposed to efficiently develop BIM models of masonry arch bridges, with the objective of generating databases (Level of Information, LoI) associated with 3D geometrical models, incorporating different levels of detail (LoD). The use of parametric libraries is implemented to develop the models. The first step is to perform an experimental campaign to obtain initial data. With these data and the parametric library previously created, a parametric model of the structure is generated (low LoD). A map of the vulnerable areas of the structure is created on this model and the available information is linked to it. A structural model (high LoD) is also created and calibrated. With all this information, an HBIM file is generated. A second experimental campaign is performed on the structure using non-destructive techniques. Point clouds, OMA data, photographs, and visual inspections from the different experimental campaigns are compared to the original HBIM file. In addition, to validate the usefulness of the parametric library, its implementation is carried out on three other historic masonry structures. This comprehensive and technologically advanced approach proves to be a promising strategy for the conservation and sustainable management of architectural heritage.