Assessment of the seismic vulnerability of the building stock in the earthquake-prone Marmara region of Turkey is of growing importance since such information is needed for reliable estimation of the losses that possible future earthquakes are likely to induce. The outcome of such loss assessment exercises can be used in planning of urban/regional-scale earthquake protection strategies; this is a priority in Turkey, particularly following the destructive earthquakes of 1999. Considering the size of the building inventory, Istanbul and its surrounding area is a case for which it is not easy to determine the structural properties and characteristics of the building stock. In this paper, geometrical, functional and material properties of the building stock in the northern Marmara Region, particularly around Istanbul, have been investigated and evaluated for use in loss estimation models and other types of statistic- or probability-based studies. In order to do that, the existing reinforced concrete (RC) stock has been classified as 'compliant' or 'non-compliant' buildings, dual (frame-wall) or frame structures and emergent or embedded-beam systems. In addition to the statistical parameters such as mean values, standard deviations, etc., probability density functions and their goodness-of-fit have also been investigated for all types of parameters. Functionalities such as purpose of use and floor area properties have been defined. Concrete properties of existing and recently constructed buildings and also characteristics of 220 and 420 MPa types of steel have been documented. Finally, the financial effects of retrofitting operations and damage repair have been investigated. © 2007 Elsevier Ltd. All rights reserved.
MULTIFILE
The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
This paper investigates the limits and efficacies of the Fiber Reinforced Polymer (FRP) material for strengthening mid-rise RC buildings against seismic actions. Turkey, the region of the highest seismic risk in Europe, is chosen as the case-study country, the building stock of which consists in its vast majority of mid-rise RC residential and/or commercial buildings. Strengthening with traditional methods is usually applied in most projects, as ordinary construction materials and no specialized workmanship are required. However, in cases of tight time constraints, architectural limitations, durability issues or higher demand for ductile performance, FRP material is often opted for since the most recent Turkish Earthquake Code allows engineers to employ this advanced-technology product to overcome issues of inadequate ductility or shear capacity of existing RC buildings. The paper compares strengthening of a characteristically typical mid-rise Turkish RC building by two methods, i.e., traditional column jacketing and FRP strengthening, evaluating their effectiveness with respect to the requirements of the Turkish Earthquake Code. The effect of FRP confinement is explicitly taken into account in the numerical model, unlike the common procedure followed according to which the demand on un-strengthened members is established and then mere section analyses are employed to meet the additional demands.
A fast growing percentage (currently 75% ) of the EU population lives in urban areas, using 70% of available energy resources. In the global competition for talent, growth and investments, quality of city life and the attractiveness of cities as environments for learning, innovation, doing business and job creation, are now the key parameters for success. Therefore cities need to provide solutions to significantly increase their overall energy and resource efficiency through actions addressing the building stock, energy systems, mobility, and air quality.The European Energy Union of 2015 aims to ensure secure, affordable and climate-friendly energy for EU citizens and businesses among others, by bringing new technologies and renewed infrastructure to cut household bills, create jobs and boost growth, for achieving a sustainable, low carbon and environmentally friendly economy, putting Europe at the forefront of renewable energy production and winning the fight against global warming.However, the retail market is not functioning properly. Many household consumers have too little choices of energy suppliers and too little control over their energy costs. An unacceptably high percentage of European households cannot afford to pay their energy bills. Energy infrastructure is ageing and is not adjusted to the increased production from renewables. As a consequence there is still a need to attract investments, with the current market design and national policies not setting the right incentives and providing insufficient predictability for potential investors. With an increasing share of renewable energy sources in the coming decades, the generation of electricity/energy will change drastically from present-day centralized production by gigawatt fossil-fueled plants towards decentralized generation, in cities mostly by local household and district level RES (e.g PV, wind turbines) systems operating in the level of micro-grids. With the intermittent nature of renewable energy, grid stress is a challenge. Therefore there is a need for more flexibility in the energy system. Technology can be of great help in linking resource efficiency and flexibility in energy supply and demand with innovative, inclusive and more efficient services for citizens and businesses. To realize the European targets for further growth of renewable energy in the energy market, and to exploit both on a European and global level the expected technological opportunities in a sustainable manner, city planners, administrators, universities, entrepreneurs, citizens, and all other relevant stakeholders, need to work together and be the key moving wheel of future EU cities development.Our SolutionIn the light of such a transiting environment, the need for strategies that help cities to smartly integrate technological solutions becomes more and more apparent. Given this condition and the fact that cities can act as large-scale demonstrators of integrated solutions, and want to contribute to the socially inclusive energy and mobility transition, IRIS offers an excellent opportunity to demonstrate and replicate the cities’ great potential. For more information see the HKU Smart Citieswebsite or check out the EU-website.
Globally, we face the urgent task of the transition to a climate neutral and circular society. Biobased materials are regenerative and add considerably less to the carbon stock in the atmosphere. Therefore they get high priority in several missions of the KIA theme “Energy transition and Sustainability”. In recent years significant progress has been made in biobased materials technology. In the “Circular Biobased Delta” region the Universities of Applied Sciences have grown into strong research partners in this field. However, successful business cases are few and society reacts only hesitantly. Accelerating the transition to biobased materials asks for a strategic move to a truly interdisciplinary collaboration. In response, in the Living Ecosystem programme, technological, economic and societal researchers from the three Universities of Applied Sciences (HZ, RUAS, Avans) join to form a core group. Together they will align and extend their research in shared topics such as biobased ingredients, circular building, and bioplastics. Around these topics, cross-sectoral communities within the existing regional ecosystem will be organised, connected and called upon to articulate interdisciplinary research projects and valorise the outcomes. The partners have different levels of achievement together forming a strong research group. They will share their experiences to collectively improve the volume, impact and quality of their research. In doing so they aim to become leaders within their separate disciplines and collectively evolve into an (inter)nationally recognised top-rank research community. The core group of researchers is assisted by a strong consortium, whose members represent the different topics and functions in the ecosystem. The consortium will advise the core group in defining and valorising their research. The regional ecosystem already hosts many “field labs”. The programme aims to create focus in their utilisation for an impactful programme of development, education and communication activities.
Introduction The research group Biobased Resources & Energy (BRE) of Avans focusses on recovery of valuable building blocks from low-value solid and liquid residual streams from agriculture, households and industries. For the valorisation of these residual streams, BRE looks into different biological, chemical and mechanical processes. One of the main issues in the utilisation of residual streams is economic feasibility and the recovery of multiple resources from one residual stream. Using membrane technologies in combination with biological, chemical and/or mechanical processes could offer great opportunities. Central Research Question What is the applicability of membrane technologies for valorisation of different residual streams and is it possible to integrate membrane technology in current and new biorefining projects of research group BRE: Set-up In order to reach the goal of this postdoc, 4 research questions will be answered using literature search, experimentation and modelling: 1) What membrane methods are currently (commercially) available to enhance the results of current projects in research group BRE? 2) What are the essential technical parameters for membrane separation and how can these be optimized? 3) What is the economic impact of using membrane technology in recovery of valuable building blocks from residual streams? 4) What are the effects of using membranes instead of or complementary to currently used methods on the sustainability of valorisation of residual streams? Cooperation The postdoc and the research group BRE want to extend the contact and research cooperation with (regional) businesses and (applied) universities and support and facilitate the introduction and further development of membrane technologies in the curriculum of different Avans study programmes. This will be done via internships, minor projects (together with businesses) and development of study material for courses and trainings.
