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Keywords
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- Kongress (14)
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- Ludwig (8)
- Mies van der Rohe (8)
- Moderne (8)
- OA-Publikationsfonds2018 (8)
- Optimierung (8)
- machine learning (8)
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- Deutschland <Bundesrepublik> (7)
- Erdbeben (7)
- Deep learning (6)
- Forschungskooperation (6)
- Industriebau (6)
- Journalismus (6)
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While Public-Private Partnership (PPP) is widely adopted across various sectors, it raises a question on its meagre utilisation in the housing sector. This paper, therefore, gauges the perspective of the stakeholders in the building industry towards the application of PPP in various building sectors together with housing. It assesses the performance reliability of PPP for housing by learning possible take-aways from other sectors. The role of key stakeholders in the industry becomes highly responsible for an informed understanding and decision-making. To this end, a two-tier investigation was conducted including surveys and expert interviews, with several stakeholders in the PPP industry in Europe, involving the public sector, private sector, consultants, as well as other community/user representatives.
The survey results demonstrated the success rate with PPPs, major factors important for PPPs such as profitability or end-user acceptability, the prevalent practices and trends in the PPP world, and the majority of support expressed in favour of the suitability of PPP for housing. The interviews added more detailed dimensions to the understanding of the PPP industry, its functioning and enabling the formation of a comprehensive outlook. The results present the perspective, approaches, and experiences of stakeholders over PPP practices, current trends and scenarios and their take on PPP in housing. It shall aid in understanding the challenges prevalent in the PPP approach for implementation in housing and enable the policymakers and industry stakeholders to make provisions for higher uptake to accelerate housing provision.
Despite digitization and platformization, mass media and established media companies still play a crucial role in the provision of journalistic content in democratic societies. Competition is one key driver of (media) company behavior and is considered to have an impact on the media’s performance. However, theory and empirical research are ambiguous about the relationship. The objective of this article is to empirically analyze the effect of competition on media performance in a cross-national context. We assessed media performance of media companies as the importance of journalistic goals within their stated corporate goal system. We conducted a content analysis of letters to the shareholders in annual reports of more than 50 media companies from 2000 to 2014 to operationalize journalistic goal importance. When employing a fixed effects regression analysis, as well as a fuzzy set qualitative comparative analysis, results suggest that competition has a positive effect on the importance of journalistic goals, while the existence of a strong public service media sector appears to have the effect of “crowding out” commercial media companies.
In this study, a new approach to basis of intelligent systems and machine learning algorithms is introduced for solving singular multi-pantograph differential equations (SMDEs). For the first time, a type-2 fuzzy logic based approach is formulated to find an approximated solution. The rules of the suggested type-2 fuzzy logic system (T2-FLS) are optimized by the square root cubature Kalman filter (SCKF) such that the proposed fineness function to be minimized. Furthermore, the stability and boundedness of the estimation error is proved by novel approach on basis of Lyapunov theorem. The accuracy and robustness of the suggested algorithm is verified by several statistical examinations. It is shown that the suggested method results in an accurate solution with rapid convergence and a lower computational cost.
Tall buildings have become an integral part of cities despite all their pros and cons. Some current tall buildings have several problems because of their unsuitable location; the problems include increasing density, imposing traffic on urban thoroughfares, blocking view corridors, etc. Some of these buildings have destroyed desirable views of the city. In this research, different criteria have been chosen, such as environment, access, social-economic, land-use, and physical context. These criteria and sub-criteria are prioritized and weighted by the analytic network process (ANP) based on experts’ opinions, using Super Decisions V2.8 software. On the other hand, layers corresponding to sub-criteria were made in ArcGIS 10.3 simultaneously, then via a weighted overlay (map algebra), a locating plan was created. In the next step seven hypothetical tall buildings (20 stories), in the best part of the locating plan, were considered to evaluate how much of theses hypothetical buildings would be visible (fuzzy visibility) from the street and open spaces throughout the city. These processes have been modeled by MATLAB software, and the final fuzzy visibility plan was created by ArcGIS. Fuzzy visibility results can help city managers and planners to choose which location is suitable for a tall building and how much visibility may be appropriate. The proposed model can locate tall buildings based on technical and visual criteria in the future development of the city and it can be widely used in any city as long as the criteria and weights are localized.
Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70%) and validation (30%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed.
For this paper, the problem of energy/voltage management in photovoltaic (PV)/battery systems was studied, and a new fractional-order control system on basis of type-3 (T3) fuzzy logic systems (FLSs) was developed. New fractional-order learning rules are derived for tuning of T3-FLSs such that the stability is ensured. In addition, using fractional-order calculus, the robustness was studied versus dynamic uncertainties, perturbation of irradiation, and temperature and abruptly faults in output loads, and, subsequently, new compensators were proposed. In several examinations under difficult operation conditions, such as random temperature, variable irradiation, and abrupt changes in output load, the capability of the schemed controller was verified. In addition, in comparison with other methods, such as proportional-derivative-integral (PID), sliding mode controller (SMC), passivity-based control systems (PBC), and linear quadratic regulator (LQR), the superiority of the suggested method was demonstrated.
This paper reports the formation and structure of fast setting geopolymers activated by using three sodium silicate solutions with different modules (1.6, 2.0 and 2.4) and a berlinite-type aluminum orthophosphate. By varying the concentration of the aluminum orthophosphate, different Si/Al-ratios were established (6, 3 and 2). Reaction kinetics of binders were determined by isothermal calorimetric measurements at 20 °C. X-ray diffraction analysis as well as nuclear magnetic resonance (NMR) measurements were performed on binders to determine differences in structure by varying the alkalinity of the sodium silicate solutions and the Si/Al-ratio. The calorimetric results indicated that the higher the alkalinity of the sodium silicate solution, the higher the solubility and degree of conversion of the aluminum orthophosphate. The results of X-ray diffraction and Rietveldt analysis, as well as the NMR measurements, confirmed the assumption of the calorimetric experiments that first the aluminum orthophosphate was dissolved and then a polycondensation to an amorphous aluminosilicate network occurred. The different amounts of amorphous phases formed as a function of the alkalinity of the sodium silicate solution, indicate that tetrahydroxoaluminate species were formed during the dissolution of the aluminum orthophosphate, which reduce the pH value. This led to no further dissolution of the aluminum orthophosphate, which remained unreacted.
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.
Welfare‐state transformation and entrepreneurial urban politics in Western welfare states since the late 1970s have yielded converging trends in the transformation of the dominant Fordist paradigm of social housing in terms of its societal function and institutional and spatial form. In this article I draw from a comparative case study on two cities in Germany to show that the resulting new paradigm is simultaneously shaped by the idiosyncrasies of the country's national housing regime and local housing policies. While German governments have successively limited the societal function of social housing as a legitimate instrument only for addressing exceptional housing crises, local policies on providing and organizing social housing within this framework display significant variation. However, planning and design principles dominating the spatial forms of social housing have been congruent. They may be interpreted as both an expression of the marginalization of social housing within the restructured welfare housing regime and a tool of its implementation according to the logics of entrepreneurial urban politics.
The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.
A new large‐field, high‐sensitivity, single‐mirror coincident schlieren optical instrument has been installed at the Bauhaus‐Universität Weimar for the purpose of indoor air research. Its performance is assessed by the non‐intrusive measurement of the thermal plume of a heated manikin. The schlieren system produces excellent qualitative images of the manikin's thermal plume and also quantitative data, especially schlieren velocimetry of the plume's velocity field that is derived from the digital cross‐correlation analysis of a large time sequence of schlieren images. The quantitative results are compared with thermistor and hot‐wire anemometer data obtained at discrete points in the plume. Good agreement is obtained, once the differences between path‐averaged schlieren data and planar anemometry data are reconciled.
Wind effects can be critical for the design of lifelines such as long-span bridges. The existence of a significant number of aerodynamic force models, used to assess the performance of bridges, poses an important question regarding their comparison and validation. This study utilizes a unified set of metrics for a quantitative comparison of time-histories in bridge aerodynamics with a host of characteristics. Accordingly, nine comparison metrics are included to quantify the discrepancies in local and global signal features such as phase, time-varying frequency and magnitude content, probability density, nonstationarity and nonlinearity. Among these, seven metrics available in the literature are introduced after recasting them for time-histories associated with bridge aerodynamics. Two additional metrics are established to overcome the shortcomings of the existing metrics. The performance of the comparison metrics is first assessed using generic signals with prescribed signal features. Subsequently, the metrics are applied to a practical example from bridge aerodynamics to quantify the discrepancies in the aerodynamic forces and response based on numerical and semi-analytical aerodynamic models. In this context, it is demonstrated how a discussion based on the set of comparison metrics presented here can aid a model evaluation by offering deeper insight. The outcome of the study is intended to provide a framework for quantitative comparison and validation of aerodynamic models based on the underlying physics of fluid-structure interaction. Immediate further applications are expected for the comparison of time-histories that are simulated by data-driven approaches.
Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.
Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.
The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighborhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively.
Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.
Carrier-bound titanium dioxide catalysts were used in a photocatalytic ozonation reactor for the degradation of micro-pollutants in real wastewater. A photocatalytic immersion rotary body reactor with a 36-cm disk diameter was used, and was irradiated using UV-A light-emitting diodes. The rotating disks were covered with catalysts based on stainless steel grids coated with titanium dioxide. The dosing of ozone was carried out through the liquid phase via an external enrichment and a supply system transverse to the flow direction. The influence of irradiation power and ozone dose on the degradation rate for photocatalytic ozonation was investigated. In addition, the performance of the individual processes photocatalysis and ozonation were studied. The degradation kinetics of the parent compounds were determined using liquid chromatography tandem mass spectrometry. First-order kinetics were determined for photocatalysis and photocatalytic ozonation. A maximum reaction rate of the reactor was determined, which could be achieved by both photocatalysis and photocatalytic ozonation. At a dosage of 0.4 mg /mg DOC, the maximum reaction rate could be achieved using 75% of the irradiation power used for sole photocatalysis, allowing increases in the energetic efficiency of photocatalytic wastewater treatment processes. The process of photocatalytic ozonation is suitable to remove a wide spectrum of micro-pollutants from wastewater.
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.
The growing complexity of modern practical problems puts high demand on mathematical modelling. Given that various models can be used for modelling one physical phenomenon, the role of model comparison and model choice is becoming particularly important. Methods for model comparison and model choice typically used in practical applications nowadays are computationbased, and thus time consuming and computationally costly. Therefore, it is necessary to develop other approaches to working abstractly, i.e., without computations, with mathematical models. An abstract description of mathematical models can be achieved by the help of abstract mathematics, implying formalisation of models and relations between them. In this paper, a category theory-based approach to mathematical modelling is proposed. In this way, mathematical models are formalised in the language of categories, relations between the models are formally defined and several practically relevant properties are introduced on the level of categories. Finally, an illustrative example is presented, underlying how the category-theory based approach can be used in practice. Further, all constructions presented in this paper are also discussed from a modelling point of view by making explicit the link to concrete modelling scenarios.
The derivation of nonlocal strong forms for many physical problems remains cumbersome in traditional methods. In this paper, we apply the variational principle/weighted residual method based on nonlocal operator method for the derivation of nonlocal forms for elasticity, thin plate, gradient elasticity, electro-magneto-elasticity and phase-field fracture method. The nonlocal governing equations are expressed as an integral form on support and dual-support. The first example shows that the nonlocal elasticity has the same form as dual-horizon non-ordinary state-based peridynamics. The derivation is simple and general and it can convert efficiently many local physical models into their corresponding nonlocal forms. In addition, a criterion based on the instability of the nonlocal gradient is proposed for the fracture modelling in linear elasticity. Several numerical examples are presented to validate nonlocal elasticity and the nonlocal thin plate.