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Object-Oriented Damage Information Modeling Concepts and Implementation for Bridge Inspection
(2022)
Bridges are designed to last for more than 50 years and consume up to 50% of their life-cycle costs during their operation phase. Several inspections and assessment actions are executed during this period. Bridge and damage information must be gathered, digitized, and exchanged between different stakeholders. Currently, the inspection and assessment practices rely on paper-based data collection and exchange, which is time-consuming and error-prone, and leads to loss of information. Storing and exchanging damage and building information in a digital format may lower costs and errors during inspection and assessment and support future needs, for example, immediate simulations regarding performance assessment, automated maintenance planning, and mixed reality inspections. This study focused on the concept for modeling damage information to support bridge reviews and structural analysis. Starting from the definition of multiple use cases and related requirements, the data model for damage information is defined independently from the subsequent implementation. In the next step, the implementation via an established standard is explained. Functional tests aim to identify problems in the concept and implementation. To show the capability of the final model, two example use cases are illustrated: the inspection review of the entire bridge and a finite-element analysis of a single component. Main results are the definition of necessary damage data, an object-oriented damage model, which supports multiple use cases, and the implementation of the model in a standard. Furthermore, the tests have shown that the standard is suitable to deliver damage information; however, several software programs lack proper implementation of the standard.
In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems.
The Gated Community (GC) phenomenon in Latin American cities has become an inherent element of their urban development, despite academical debate, their approach thrives within the housing market; not surprisingly, as some of the premises on which GCs are based, namely safety, control and supervision intersperse seamlessly with the insecure conditions of the contexts from which they arise. The current security crisis in Mexico, triggered in 2006 by the so-called war on drugs, has reached its peak with the highest insecurity rates in decades, representing a unique chance to study these interactions. Although the leading term of this research, Urban Agoraphobia, implies a causal dichotomy between the rise in the sense of fear amongst citizens and housing confinement as lineal consequence, I acknowledge that GCs represent a complex phenomenon, a hub of diverse factors and multidimensional processes held on four fundamental levels: global, social, individual and state-related. The focus of this dissertation is set on the individual plane and contributes, from the analysis of the GC’s resident’s perspective, experiences and perceptions, to a debate that has usually been limited to the scrutiny of other drivers, disregarding the role of dweller’s underlying fears, motivations and concerns. Assuming that the current ruling security model in Mexico tends to empower its commodification rather than its collective quality, this research draws upon the use of a methodological triangulation, along conceptual and contextual analyses, to test the hypothesis that insecurity plays an increasingly major role, leading citizens into the belief that acquiring a household in a controlled and surveilled community represents a counterweight against the feared environment of the open city. The focus of the analysis lies on the internal hatch of community ties as potential palliative for the provision of a sense of security, aiming to transcend the unidimensional discourse of GCs as defined mainly by their defensive apparatus. Residents’ perspectives acquired through ethnographical analyses may provide the chance to gain an essential view into a phenomenon that further consolidates without a critical study of its actual implications, not only for Mexican cities, but also for the Latin American and global contexts.
Multi-criteria decision analysis (MCDA) is an established methodology to support the decision-making of multi-objective problems. For conducting an MCDA, in most cases, a set of objectives (SOO) is required, which consists of a hierarchical structure comprised of objectives, criteria, and indicators. The development of an SOO is usually based on moderated development processes requiring high organizational and cognitive effort from all stakeholders involved. This article proposes elementary interactions as a key paradigm of an algorithm-driven development process for an SOO that requires little moderation efforts. Elementary interactions are self-contained information requests that may be answered with little cognitive effort. The pairwise comparison of elements in the well-known analytical hierarchical process (AHP) is an example of an elementary interaction. Each elementary interaction in the development process presented contributes to the stepwise development of an SOO. Based on the hypothesis that an SOO may be developed exclusively using elementary interactions (EIs), a concept for a multi-user platform is proposed. Essential components of the platform are a Model Aggregator, an Elementary Interaction Stream Generator, a Participant Manager, and a Discussion Forum. While the latter component serves the professional exchange of the participants, the first three components are intended to be automatable by algorithms. The platform concept proposed has been evaluated partly in an explorative validation study demonstrating the general functionality of the algorithms outlined. In summary, the platform concept suggested demonstrates the potential to ease SOO development processes as the platform concept does not restrict the application domain; it is intended to work with little administration moderation efforts, and it supports the further development of an existing SOO in the event of changes in external conditions. The algorithm-driven development of SOOs proposed in this article may ease the development of MCDA applications and, thus, may have a positive effect on the spread of MCDA applications.
Multi-criteria decision analysis (MCDA) is an established methodology to support the decision-making of multi-objective problems. For conducting an MCDA, in most cases, a set of objectives (SOO) is required, which consists of a hierarchical structure comprised of objectives, criteria, and indicators. The development of an SOO is usually based on moderated development processes requiring high organizational and cognitive effort from all stakeholders involved. This article proposes elementary interactions as a key paradigm of an algorithm-driven development process for an SOO that requires little moderation efforts. Elementary interactions are self-contained information requests that may be answered with little cognitive effort. The pairwise comparison of elements in the well-known analytical hierarchical process (AHP) is an example of an elementary interaction. Each elementary interaction in the development process presented contributes to the stepwise development of an SOO. Based on the hypothesis that an SOO may be developed exclusively using elementary interactions (EIs), a concept for a multi-user platform is proposed. Essential components of the platform are a Model Aggregator, an Elementary Interaction Stream Generator, a Participant Manager, and a Discussion Forum. While the latter component serves the professional exchange of the participants, the first three components are intended to be automatable by algorithms. The platform concept proposed has been evaluated partly in an explorative validation study demonstrating the general functionality of the algorithms outlined. In summary, the platform concept suggested demonstrates the potential to ease SOO development processes as the platform concept does not restrict the application domain; it is intended to work with little administration moderation efforts, and it supports the further development of an existing SOO in the event of changes in external conditions. The algorithm-driven development of SOOs proposed in this article may ease the development of MCDA applications and, thus, may have a positive effect on the spread of MCDA applications.
Bauablaufplänen kommt bei der Realisierung von Bauprojekten eine zentrale Rolle zu. Sie dienen der Koordination von Schnittstellen und bilden für die am Projekt Beteiligten die Grundlage für ihre individuelle Planung. Eine verlässliche Terminplanung ist daher von großer Bedeutung, tatsächlich sind aber gerade Bauablaufpläne für ihre Unzuverlässigkeit bekannt.
Aufgrund der langen Vorlaufzeiten bei der Planung von Bauprojekten sind zum Zeitpunkt der Planung viele Informationen nur als Schätzwerte bekannt. Auf der Grundlage dieser geschätzten und damit mit Unsicherheiten behafteten Daten werden im Bauwesen deterministische Terminpläne erstellt. Kommt es während der Realisierung zu Diskrepanzen zwischen Schätzungen und Realität, erfordert dies die Anpassung der Pläne. Aufgrund zahlreicher Abhängigkeiten zwischen den geplanten Aktivitäten können einzelne Planänderungen vielfältige weitere Änderungen und Anpassungen nach sich ziehen und damit einen reibungslosen Projektablauf gefährden.
In dieser Arbeit wird ein Vorgehen entwickelt, welches Bauablaufpläne erzeugt, die im Rahmen der durch das Projekt definierten Abhängigkeiten und Randbedingungen in der Lage sind, Änderungen möglichst gut zu absorbieren. Solche Pläne, die bei auftretenden Änderungen vergleichsweise geringe Anpassungen des Terminplans erfordern, werden hier als robust bezeichnet.
Ausgehend von Verfahren der Projektplanung und Methoden zur Berücksichtigung von Unsicherheiten werden deterministische Terminpläne bezüglich ihres Verhaltens bei eintretenden Änderungen betrachtet. Hierfür werden zunächst mögliche Unsicherheiten als Ursachen für Änderungen benannt und mathematisch abgebildet. Damit kann das Verhalten von Abläufen für mögliche Änderungen betrachtet werden, indem die durch Änderungen erzwungenen angepassten Terminpläne simuliert werden. Für diese Monte-Carlo-Simulationen der angepassten Terminpläne wird sichergestellt, dass die angepassten Terminpläne logische Weiterentwicklungen des deterministischen Terminplans darstellen. Auf der Grundlage dieser Untersuchungen wird ein stochastisches Maß zur Quantifizierung der Robustheit erarbeitet, welches die Fähigkeit eines Planes, Änderungen zu absorbieren, beschreibt. Damit ist es möglich, Terminpläne bezüglich ihrer Robustheit zu vergleichen.
Das entwickelte Verfahren zur Quantifizierung der Robustheit wird in einem Optimierungsverfahren auf Basis Genetischer Algorithmen angewendet, um gezielt robuste Terminpläne zu erzeugen. An Beispielen werden die Methoden demonstriert und ihre Wirksamkeit nachgewiesen.
Plastic structural analysis may be applied without any difficulty and with little effort for structural member verifications with regard to lateral torsional buckling of doubly symmetric rolled I sections. Suchlike analyses can be performed based on the plastic zone theory, specifically using finite beam elements with seven degrees of freedom and 2nd order theory considering material nonlinearity. The existing Eurocode enables these approaches and the coming-up generation will provide corresponding regulations in EN 1993-1-14. The investigations allow the determination of computationally accurate limit loads, which are determined in the present paper for selected structural systems with different sets of parameters, such as length, steel grade and cross section types. The results are compared to approximations gained by more sophisticated FEM analyses (commercial software Ansys Workbench applying solid elements) for reasons of verification/validation. In this course, differences in the results of the numerical models are addressed and discussed. In addition, results are compared to resistances obtained by common design regulations based on reduction factors χlt including regulations of EN 1993-1-1 (including German National Annex) as well as prEN 1993-1-1: 2020-08 (proposed new Eurocode generation). Concluding, correlations of results and their advantages as well as disadvantages are discussed.
The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency
Real-world labs hold the potential to catalyse rapid urban transformations through real-world experimentation. Characterised by a rather radical, responsive, and location-specific nature, real-world labs face constraints in the scaling of experimental knowledge. To make a significant contribution to urban transformation, the produced knowledge must go beyond the level of a building, street, or small district where real-world experiments are conducted. Thus, a conflict arises between experimental boundaries and the stimulation of broader implications. The challenges of scaling experimental knowledge have been recognised as a problem, but remain largely unexplained. Based on this, the article will discuss the applicability of the “typology of amplification processes” by Lam et al. (2020) to explore and evaluate the potential of scaling experimental knowledge from real-world labs. The application of the typology is exemplified in the case of the Bauhaus.MobilityLab. The Bauhaus.MobilityLab takes a unique approach by testing and developing cross-sectoral mobility, energy, and logistics solutions with a distinct focus on scaling knowledge and innovation. For this case study, different qualitative research techniques are combined according to “within-method triangulation” and synthesised in a strengths, weaknesses, opportunities, and threats (SWOT) analysis. The analysis of the Bauhaus.MobilityLab proves that the “typology of amplification processes” is useful as a systematic approach to identifying and evaluating the potential of scaling experimental knowledge.
Vertical green system for gray water treatment: Analysis of the VertiKKA-module in a field test
(2022)
This work presents a modular Vertical Green System (VGS) for gray water treatment, developed at the Bauhaus-Universität Weimar. The concept was transformed into a field study with four modules built and tested with synthetic gray water. Each module set contains a small and larger module with the same treatment substrate and was fed hourly. A combination of lightweight structural material and biochar of agricultural residues and wood chips was used as the treatment substrate. In this article, we present the first 18 weeks of operation. Regarding the treatment efficiency, the parameters chemical oxygen demand (COD), total phosphorous (TP), ortho-phosphate (ortho-P), total bound nitrogen (TNb), ammonium nitrogen (NH4-N), and nitrate nitrogen (NO3-N) were analyzed and are presented in this work. The results of the modules with agricultural residues are promising. Up to 92% COD reduction is stated in the data. The phosphate and nitrogen fractions are reduced significantly in these modules. By contrast, the modules with wood chips reduce only 67% of the incoming COD and respectively less regarding phosphates and the nitrogen fraction.