56 Bauwesen
Refine
Document Type
- Article (31) (remove)
Institute
- Institut für Strukturmechanik (ISM) (10)
- Junior-Professur Computational Architecture (8)
- Professur Informatik in der Architektur (7)
- Professur Stahl- und Hybridbau (3)
- Institut für Konstruktiven Ingenieurbau (IKI) (1)
- Materialforschungs- und -prüfanstalt an der Bauhaus-Universität (1)
- Professur Bauphysik (1)
- Professur Modellierung und Simulation - Konstruktion (1)
Keywords
- OA-Publikationsfonds2022 (7)
- OA-Publikationsfonds2020 (5)
- Erdbeben (3)
- Maschinelles Lernen (3)
- damaged buildings (3)
- Architektur (2)
- CAD (2)
- Computational Design (2)
- Computational Urban Design (2)
- Lebenszyklus (2)
For the safe and efficient operation of dams, frequent monitoring and maintenance are required. These are usually expensive, time consuming, and cumbersome. To alleviate these issues, we propose applying a wave-based scheme for the location and quantification of damages in dams.
To obtain high-resolution “interpretable” images of the damaged regions, we drew inspiration from non-linear full-multigrid methods for inverse problems and applied a new cyclic multi-stage full-waveform inversion (FWI) scheme. Our approach is less susceptible to the stability issues faced by the standard FWI scheme when dealing with ill-posed problems. In this paper, we first selected an optimal acquisition setup and then applied synthetic data to demonstrate the capability of our approach in identifying a series of anomalies in dams by a mixture of reflection and transmission tomography. The results had sufficient robustness, showing the prospects of application in the field of non-destructive testing of dams.
A safe and economic structural design based on the semi-probabilistic concept requires statistically representative safety elements, such as characteristic values, design values, and partial safety factors. Regarding climate loads, the safety levels of current design codes strongly reflect experiences based on former measurements and investigations assuming stationary conditions, i.e. involving constant frequencies and intensities. However, due to climate change, occurrence of corresponding extreme weather events is expected to alter in the future influencing the reliability and safety of structures and their components. Based on established approaches, a systematically refined data-driven methodology for the determination of design parameters considering nonstationarity as well as standardized targets of structural reliability or safety, respectively, is therefore proposed. The presented procedure picks up fundamentals of European standardization and extends them with respect to nonstationarity by applying a shifting time window method. Taking projected snow loads into account, the application of the method is exemplarily demonstrated and various influencing parameters are discussed.
Design-related reassessment of structures integrating Bayesian updating of model safety factors
(2022)
In the semi-probabilistic approach of structural design, the partial safety factors are defined by considering some degree of uncertainties to actions and resistance, associated with the parameters’ stochastic nature. However, uncertainties for individual structures can be better examined by incorporating measurement data provided by sensors from an installed health monitoring scheme. In this context, the current study proposes an approach to revise the partial safety factor for existing structures on the action side, γE by integrating Bayesian model updating. A simple numerical example of a beam-like structure with artificially generated measurement data is used such that the influence of different sensor setups and data uncertainties on revising the safety factors can be investigated. It is revealed that the health monitoring system can reassess the current capacity reserve of the structure by updating the design safety factors, resulting in a better life cycle assessment of structures. The outcome is furthermore verified by analysing a real life small railway steel bridge ensuring the applicability of the proposed method to practical applications.
Bolted connections are widely employed in structures like transmission poles, wind turbines, and television (TV) towers. The behaviour of bolted connections is often complex and plays a significant role in the overall dynamic characteristics of the structure. The goal of this work is to conduct a fatigue lifecycle assessment of such a bolted connection block of a 193 m tall TV tower, for which 205 days of real measurement data have been obtained from the installed monitoring devices. Based on the recorded data, the best-fit stochastic wind distribution for 50 years, the decisive wind action, and the locations to carry out the fatigue analysis have been decided. A 3D beam model of the entire tower is developed to extract the nodal forces corresponding to the connection block location under various mean wind speeds, which is later coupled with a detailed complex finite element model of the connection block, with over three million degrees of freedom, for acquiring stress histories on some pre-selected bolts. The random stress histories are analysed using the rainflow counting algorithm (RCA) and the damage is estimated using Palmgren-Miner's damage accumulation law. A modification is proposed to integrate the loading sequence effect into the RCA, which otherwise is ignored, and the differences between the two RCAs are investigated in terms of the accumulated damage.
The fracture of microcapsules is an important issue to release the healing agent for healing the cracks in encapsulation-based self-healing concrete. The capsular clustering generated from the concrete mixing process is considered one of the critical factors in the fracture mechanism. Since there is a lack of studies in the literature regarding this issue, the design of self-healing concrete cannot be made without an appropriate modelling strategy. In this paper, the effects of microcapsule size and clustering on the fractured microcapsules are studied computationally. A simple 2D computational modelling approach is developed based on the eXtended Finite Element Method (XFEM) and cohesive surface technique. The proposed model shows that the microcapsule size and clustering have significant roles in governing the load-carrying capacity and the crack propagation pattern and determines whether the microcapsule will be fractured or debonded from the concrete matrix. The higher the microcapsule circumferential contact length, the higher the load-carrying capacity. When it is lower than 25% of the microcapsule circumference, it will result in a greater possibility for the debonding of the microcapsule from the concrete. The greater the core/shell ratio (smaller shell thickness), the greater the likelihood of microcapsules being fractured.
Data acquisition systems and methods to capture high-resolution images or reconstruct 3D point clouds of existing structures are an effective way to document their as-is condition. These methods enable a detailed analysis of building surfaces, providing precise 3D representations. However, for the condition assessment and documentation, damages are mainly annotated in 2D representations, such as images, orthophotos, or technical drawings, which do not allow for the application of a 3D workflow or automated comparisons of multitemporal datasets. In the available software for building heritage data management and analysis, a wide range of annotation and evaluation functions are available, but they also lack integrated post-processing methods and systematic workflows. The article presents novel methods developed to facilitate such automated 3D workflows and validates them on a small historic church building in Thuringia, Germany. Post-processing steps using photogrammetric 3D reconstruction data along with imagery were implemented, which show the possibilities of integrating 2D annotations into 3D documentations. Further, the application of voxel-based methods on the dataset enables the evaluation of geometrical changes of multitemporal annotations in different states and the assignment to elements of scans or building models. The proposed workflow also highlights the potential of these methods for condition assessment and planning of restoration work, as well as the possibility to represent the analysis results in standardised building model formats.
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
A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.
One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel.
To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.
The development of a hydro-mechanically coupled Coupled-Eulerian–Lagrangian (CEL) method and its application to the back-analysisof vibratory pile driving model tests in water-saturated sand is presented. The predicted pile penetration using this approachis in good agreement with the results of the model tests as well as with fully Lagrangian simulations. In terms of pore water pressure, however, the results of the CEL simulation show a slightly worse accordance with the model tests compared to the Lagrangian simulation. Some shortcomings of the hydro-mechanically coupled CEL method in case of frictional contact problems and pore fluids with high bulk modulus are discussed. Lastly, the CEL method is applied to the simulation of vibratory driving of open-profile piles under partially drained conditions to study installation-induced changes in the soil state. It is concluded that the proposed method is capable of realistically reproducing the most important mechanisms in the soil during the driving process despite its addressed shortcomings.
The Marmara Region (NW Turkey) has experienced significant earthquakes (M > 7.0) to date. A destructive earthquake is also expected in the region. To determine the effect of the specific design spectrum, eleven provinces located in the region were chosen according to the Turkey Earthquake Building Code updated in 2019. Additionally, the differences between the previous and updated regulations of the country were investigated. Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) were obtained for each province by using earthquake ground motion levels with 2%, 10%, 50%, and 68% probability of exceedance in 50-year periods. The PGA values in the region range from 0.16 to 0.7 g for earthquakes with a return period of 475 years. For each province, a sample of a reinforced-concrete building having two different numbers of stories with the same ground and structural characteristics was chosen. Static adaptive pushover analyses were performed for the sample reinforced-concrete building using each province’s design spectrum. The variations in the earthquake and structural parameters were investigated according to different geographical locations. It was determined that the site-specific design spectrum significantly influences target displacements for performance-based assessments of buildings due to seismicity characteristics of the studied geographic location.
A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
(2020)
Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.
Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared.
Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritizes their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. This might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of reinforced concrete buildings from the Bingöl and Düzce earthquakes in Turkey.
Occupant needs with regard to residential buildings are not well known due to a lack of representative scientific studies. To improve the lack of data, a large scale study was carried out using a Post Occupancy Evaluation of 1,416 building occupants. Several criteria describing the needs of occupants were evaluated with regard to their subjective level of relevance. Additionally, we investigated the degree to which deficiencies subjectively exist, and the degree to which occupants were able to accept them. From the data obtained, a hierarchy of criteria was created. It was found that building occupants ranked the physiological needs of air quality and thermal comfort the highest. Health hazards such as mould and contaminated building materials were unacceptable for occupants, while other deficiencies were more likely to be tolerated. Occupant satisfaction was also investigated. We found that most occupants can be classified as satisfied, although some differences do exist between different populations. To explain the relationship between the constructs of what we call relevance, acceptance, deficiency and satisfaction, we then created an explanatory model. Using correlation and regression analysis, the validity of the model was then confirmed by applying the collected data. The results of the study are both relevant in shaping further research and in providing guidance on how to maximize tenant satisfaction in real estate management.
As part of an international research project – funded by the European Union – capillary glasses for facades are being developed exploiting storage energy by means of fluids flowing through the capillaries. To meet highest visual demands, acrylate adhesives and EVA films are tested as possible bonding materials for the glass setup. Especially non-destructive methods (visual analysis, analysis of birefringent properties and computed tomographic data) are applied to evaluate failure patterns as well as the long-term behavior considering climatic influences. The experimental investigations are presented after different loading periods, providing information of failure developments. In addition, detailed information and scientific findings on the application of computed tomographic analyses are presented.
Augmented Urban Model: Ein Tangible User Interface zur Unterstützung von Stadtplanungsprozessen
(2011)
Im architektonischen und städtebaulichen Kontext erfüllen physische und digitale Modelle aufgrund ihrer weitgehend komplementären Eigenschaften und Qualitäten unterschiedliche, nicht verknüpfte Aufgaben und Funktionen im Entwurfs- und Planungsprozess. Während physische Modelle vor allem als Darstellungs- und Kommunikationsmittel aber auch als Arbeitswerkzeug genutzt werden, unterstützen digitale Modelle darüber hinaus die Evaluation eines Entwurfs durch computergestützte Analyse- und Simulationstechniken.
Analysiert wurden im Rahmen der in diesem Arbeitspapier vorgestellten Arbeit neben dem Einsatz des Modells als analogem und digitalem Werkzeug im Entwurf die Bedeutung des Modells für den Arbeitsprozess sowie Vorbilder aus dem Bereich der Tangible User Interfaces mit Bezug zu Architek¬tur und Städtebau. Aus diesen Betrachtungen heraus wurde ein Prototyp entwickelt, das Augmented Urban Model, das unter anderem auf den frühen Projekten und Forschungsansätzen aus dem Gebiet der Tangible User Interfaces aufsetzt, wie dem metaDESK von Ullmer und Ishii und dem Urban Planning Tool Urp von Underkoffler und Ishii.
Das Augmented Urban Model zielt darauf ab, die im aktuellen Entwurfs- und Planungsprozess fehlende Brücke zwischen realen und digitalen Modellwelten zu schlagen und gleichzeitig eine neue tangible Benutzerschnittstelle zu schaffen, welche die Manipulation von und die Interaktion mit digitalen Daten im realen Raum ermöglicht.
Dieses Arbeitspapier beschreibt, wie ausgehend von einem vorhandenen Straßennetzwerk Bebauungsareale mithilfe von Unterteilungsalgorithmen automatisch umgelegt, d.h. in Grundstücke unterteilt, und anschließend auf Basis verschiedener städtebaulicher Typen bebaut werden können. Die Unterteilung von Bebauungsarealen und die Generierung von Bebauungsstrukturen unterliegen dabei bestimmten stadtplanerischen Einschränkungen, Vorgaben und Parametern. Ziel ist es aus den dargestellten Untersuchungen heraus ein Vorschlagssystem für stadtplanerische Entwürfe zu entwickeln, das anhand der Umsetzung eines ersten Softwareprototyps zur Generierung von Stadtstrukturen weiter diskutiert wird.
Aktionsräume in Dresden
(2012)
In vorliegender Studie werden die Aktionsräume von Befragten in Dresden über eine standardisierte Befragung (n=360) untersucht. Die den Aktionsräumen zugrundeliegenden Aktivitäten werden unterschieden in Einkaufen für den täglichen Bedarf, Ausgehen (z.B. in Café, Kneipe, Gaststätte), Erholung im Freien (z.B. spazieren gehen, Nutzung von Grünanlagen) und private Geselligkeit (z.B. Feiern, Besuch von Verwandten/Freunden). Der Aktionsradius wird unterschieden in Wohnviertel, Nachbarviertel und sonstiges weiteres Stadtgebiet. Um aus den vier betrachteten Aktivitäten einen umfassenden Kennwert für den durchschnittlichen Aktionsradius eines Befragten zu bilden, wird ein Modell für den Kennwert eines Aktionsradius entwickelt. Die Studie kommt zu dem Ergebnis, dass das Alter der Befragten einen signifikanten – wenn auch geringen – Einfluss auf den Aktionsradius hat. Das Haushaltsnettoeinkommen hat einen mit Einschränkung signifikanten, ebenfalls geringen Einfluss auf alltägliche Aktivitäten der Befragten.
In vorliegender Studie werden die Wohnstandortpräferenzen der Sinus-Milieugruppen in Dresden über eine standardisierte Befragung (n=318) untersucht. Es wird unterschieden zwischen handlungsleitenden Wohnstandortpräferenzen, die durch Anhaltspunkte auf der Handlungsebene stärker in Betracht gezogen werden sollten, und Wohnstandortpräferenzen, welche eher orientierenden Charakter haben. Die Wohnstandortpräferenzen werden untersucht anhand der Kategorien Ausstattung/Zustand der Wohnung/des näheren Wohnumfeldes, Versorgungsstruktur, soziales Umfeld, Baustrukturtyp, Ortsgebundenheit sowie des Aspektes des Images eines Stadtviertels. Um die Befragten den Sinus-Milieugruppen zuordnen zu können, wird ein Lebensweltsegment-Modell entwickelt, welches den Anspruch hat, die Sinus-Milieugruppen in der Tendenz abzubilden. Die Studie kommt zu dem Ergebnis, dass die Angehörigen der verschiedenen Lebensweltsegmente in jeder Kategorie - wenn auch z.T. auf geringerem Niveau - signifikante Unterschiede in der Bewertung einzelner Wohnstandortpräferenzen aufweisen.