HFTor - HfT Open Repository (Hochschule für Technik Stuttgart)
Not a member yet
404 research outputs found
Sort by
On the Prospects of the Building Envelope in the Context of Smart Sustainable Cities: A Brief Review
No full textAccording to the World Urbanization Prospects of the United Nations (United Nations, Department of Economic and Social Affairs 2018), about 68% of the overall expected world population is going to live in urban areas and cities by the year 2050. The task of transforming modern cities towards a more sustainable and ecological as well as socially likeable environment touches a plethora of disciplines like transportation, energy infrastructure, architecture, building physics, etc. and poses an intricate challenge for architects, engineers, urban planners and social scientist alike. Thanks to the technological evolution, the realization of smart city concepts and solutions has become a viable option to contribute to this goal in a significant manner. The building envelope as an indispensable part of human dwellings and working space has historically mostly taken the classic functionalities of resembling aesthetic expression and providing physical protection. Recent and upcoming technological developments will enable a shift in the role of the building envelope from its mere classical functionalities towards additional contributions to the sustainability and livability of the environment. The prospects of these contributions range from increasing the thermal and visual comfort of the inhabitants and the surrounding environment by adaptive architectural measures towards potential energy saving by energy harvesting devices and synergetic processing and treatment of material flows of the building to provide conditioned air, preprocessed water and even food. In this short essay, we will elaborate on the phenomenon of smart city in general and the role of the building envelope in the context of modern smart city development and its potential on the improvement on different aspects of life and urban environment
Deep Learning Methods for Extracting Object-Oriented Models of Building Interiors from Images
No full textIn this chapter, we present an approach of enriching photogrammetric point clouds with semantic information extracted from images of digital cameras or smartphones to enable a later automation of BIM modelling with object-oriented models. Based on the DeepLabv3+ architecture, we extract building components and objects of interiors in full 3D. During the photogrammetric reconstruction, we project the segmented categories derived from the images into the point cloud. Based on the semantic information, we align the point cloud, correct the scale and extract further information. The combined extraction of geometric and semantic information yields a high potential for automated BIM model reconstruction
Airborne Sound Insulation of Sustainable Building Facades
No full textTwo trends are currently leading to an increased risk of indoor noise pollution. Firstly, urban densification causes traffic noise sources to be closer to the building facades which makes them louder at the facades. Secondly, airtightness of buildings, due to energy regulations, leads to the need of natural or mechanical ventilation to ensure a “healthy” indoor air quality, thereby allowing noise to easily pass from outdoors to indoors. In the case of mechanical ventilation, an additional noise source is also created. This study investigates the risk reduction of an indoor noise problem by optimizing the facade elements regarding sound insulation. Noise levels of different transportation noise sources (cars, trucks, trains) are used to calculate the resulting indoor noise levels after passing through the facade elements. The amount of noise transmitted into the indoors is dependent on the frequency spectra of the sources and of the sound reduction properties of the facade elements. Facade elements such as masonry walls, open windows, and ventilators are investigated and modified regarding their sound insulation properties. Through passive means, the weighted sound reduction index of an open window and an open ventilator was increased by 12 dB and 3 dB, respectively. Also, the indoor self-noise of the ventilator was investigated and reduced for different airflow rates
Machine Learning Models for Predicting Indoor Air Temperature of Smart Building
No full textThe indoor air temperature is one of the key factors to improve the performance of energy efficiency of buildings and quality of life in a very smart IoT environment. Therefore, a periodic and accurate prediction of the minimum and maximum indoor air temperature allows taking necessary precautions to handle the variations’ impact and tendencies. During this assessment, we developed minimum and maximum indoor air temperature prediction models using multiple statistical regression (MLR), multilayered perceptron (MLP), and random forest (RF, where Rf is achieved once with tree depth 10 (RFdepth10), and once with tree depth 50 (RFdepth50)). The study was conducted at a building located within the University of Applied Sciences, Stuttgart, in Germany. Sensors were accustomed to aggregate data, which were used because of the input variables for the prediction. The variables are outdoor air temperature, indoor air temperature, humidity, and heating temperature. Performance of the models was evaluated with the coefficient of determination
and therefore the root means square error (RMSE). The simulation results showed that the prediction by the MLP algorithm, based on minimum indoor air temperature models and also maximum indoor air temperature models, provides better accuracy with the very best
and lowest RMSE in the independent test dataset. This survey developed a straightforward and powerful MLP model to predict the minimum and therefore the maximum indoor air temperature, which may integrate into smart building management system technology in the future
Comparative analysis of different methodologies and datasets for Energy Performance Labelling of buildings
No full textAccording to studies carried out by European Commission Directorate-General for Energy (DG ENER), buildings are responsible for approximately 40% of the primary energy consumption in Europe. Therefore, there is a vital need to take actions to improve the energy efficiency of the building stock. Predictions of the heat demand at the building level, for an entire district or city, could provide valuable support to different stakeholders involved in the energy efficiency policy cycle. However, these predictions are hampered by the lack of standardised calculation methodologies and interoperable building data to perform energy simulations. Another drawback is the low degree of comparability of the predictions. The latter has different causes: different calculation methodologies, diverse accuracy of building data, heterogeneous encoding of data and different ways of representing and visualising data. Predictions of energy heat demand using the simulation software SimStadt have been produced, analysed and compared in four different case studies in three different Member States. The simulations were done with 3D building data of different accuracy and from different sources, which made it possible to identify significant causes of mismatch between simulations and real consumption scenarios. Several mapping exercises between the CityGML standard and the INSPIRE Directive data models have been documented to improve the interoperability of input and output datasets used in the simulations. The conclusions drawn can support stakeholders involved in energy policy cycle aiming to assess the energy performance of their building stock in different geographical areas. A preliminary costs and benefits analysis of the assessment can be done re-using the methodology described in the report. Five recommendations have been also formulated, suggested by the potential implications that the conclusions of the report may have on several policy-related discussions regarding the improvement of the energy efficiency of the building stock
Watershed delineation in South Bengal Ganges Delta Region of Bangladesh using satellite imagery and digital elevation model
No full textCoastal Bangladesh has experienced large scale changes in erosion and deposition in the Meghna Estuary and the big islands due to the Ganges–Brahmaputra-Meghna stream background. Also, the coastal area is prone to natural disasters almost in every year which creates a change in the ground water level, increases the surface water infltration, soil salinity, and food level. Considering these facts of the coastal area of Bangladesh, watershed delineation can contribute to proper planning and management of watershed to mitigate the surface and groundwater problems. Therefore, in this paper GIS and remote sensing techniques were used to identify the exact water course using spatial data to know the current watershed condition of the South Ganges Delta Region of Bangladesh. Here, Hydrology Toolset was utilized to analyze and identify correct watershed fow direction, network density, and confuence thresholds using digital elevation model (DEM) of the study area. The well-known D8 algorithm deployed to calculate the stream fow from each cell to its downslope neighbor and 100–1500 thresholds to determine the fow directions and transform the streams into line features for watershed network density measurement. The results showed that the length and density of the networks were proportional to the threshold. In consequence, the density of the stream network increased dramatically with the soaring of thresholds. Therefore, the results also revealed that when the convergence threshold set to 900, the extracted stream network appeared the closest to the exact
water fow in the research area. It showed various sharp fows of the stream network, their length and density, as well as the convergence threshold. The fndings of this study can help to quantify the watershed basin and river fow watercourses that can contribute to plan and manage future food forecasting method of the study region
Literature review on sources of interference and proposed solutions for RFID installations in complex production and logistics processes in the automotive industry
No full textRFID (Radio Frequency Identification) is used in complex production and logistics processes
in the automotive industry. One of the biggest challenges with RFID installations is the bad performance,
especially the low read rates. One reason for the low read rates are sources of interference. In order to reduce
sources of interference in RFID installations, these and possible solutions must first be known. Therefore,
in this paper a literature review was conducted to identify the known sources of interference and proposed
solutions for RFID installations in the automotive sector. These were divided into the four categories RFID
components, electrical signals, physical environment and environmental influences. In addition, this paper
presents a procedure for reducing sources of interference in RFID installations. With this information, a
company can build up know-how and support the reduction of interference sources in RFID installations,
which in turn increases the read rate
Implementing Learning Analytics-based Feedback in Online Laboratories — using the Example of a Remote Laboratory
No full textLaboratory-based learning in practical, lab-based learning environments forms a central pillar of engineering education, as it promotes the practical application of theoretical knowledge and thus supports theory–practice transfer in a particular way. Over the past 15 years, laboratories for use in teaching and research have undergone a rapid transformation. This transformation is primarily reflected in the numerical increase in labs accessible online, such as remote labs, virtual labs, labs supported with augmented reality, or a combination of the aforementioned, which are also known as hybrid, mixed reality, or cross-reality labs. This opens up a wide range of opportunities for data collection, which in turn enables a wide variety of Learning Analytics (LA) applications. The use of LA-based feedback in a remote laboratory-based learning environment will be illustrated using the RFID measurement chamber laboratory at Hochschule für Technik Stuttgart (HFT)
On the Prospects of the Building Envelope in the Context of Smart Sustainable Cities: A Brief Review
No full textAccording to the World Urbanization Prospects of the United Nations (United Nations, Department of Economic and Social Affairs 2018), about 68% of the overall expected world population is going to live in urban areas and cities by the year 2050. The task of transforming modern cities towards a more sustainable and ecological as well as socially likeable environment touches a plethora of disciplines like transportation, energy infrastructure, architecture, building physics, etc. and poses an intricate challenge for architects, engineers, urban planners and social scientist alike. Thanks to the technological evolution, the realization of smart city concepts and solutions has become a viable option to contribute to this goal in a significant manner. The building envelope as an indispensable part of human dwellings and working space has historically mostly taken the classic functionalities of resembling aesthetic expression and providing physical protection. Recent and upcoming technological developments will enable a shift in the role of the building envelope from its mere classical functionalities towards additional contributions to the sustainability and livability of the environment. The prospects of these contributions range from increasing the thermal and visual comfort of the inhabitants and the surrounding environment by adaptive architectural measures towards potential energy saving by energy harvesting devices and synergetic processing and treatment of material flows of the building to provide conditioned air, preprocessed water and even food. In this short essay, we will elaborate on the phenomenon of smart city in general and the role of the building envelope in the context of modern smart city development and its potential on the improvement on different aspects of life and urban environment
Automatic Classification and Disassembly of Fasteners in Industrial 3D CAD-Scenarios
No full textThe automatic generation of (dis)assembly sequences for complex technical products is a challenging field. Complex products like vehicles consist of numerous different components. Determining the sequence using a brute-force-approach by testing all components for disassembly one after another in a loop until all components are disassembled is laborious and costly. In industrial scenarios, a large proportion of the components are fasteners. In this paper, we propose a new framework which improves the disassembly sequencing generation by prioritizing fasteners during planning. Our proposed framework comprises a preprocessing in which fasteners are identified with a convolutional neural network within a dataset and a procedure that preferentially and automatically checks fasteners for disassembly. The algorithm takes initial and unavoidable collisions of the fasteners into account. We show the effectiveness of our approach on real-world data from the automotive industry. A new synthetic dataset of fasteners for training neural networks is available