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.
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.
In this work, the degradation performance for the photocatalytic oxidation of eight micropollutants (amisulpride, benzotriazole, candesartan, carbamazepine, diclofenac, gabapentin, methlybenzotriazole, and metoprolol) within real secondary effluent was investigated using three different reactor designs. For all reactor types, the influence of irradiation power on its reaction rate and energetic efficiency was investigated. Flat cell and batch reactor showed almost similar substance specific degradation behavior. Within the immersion rotary body reactor, benzotriazole and methylbenzotriazole showed a significantly lower degradation affinity. The flat cell reactor achieved the highest mean degradation rate, with half time values ranging from 5 to 64 min with a mean of 18 min, due to its high catalysts surface to hydraulic volume ratio. The EE/O values were calculated for all micro-pollutants as well as the mean degradation rate constant of each experimental step. The lowest substance specific energy per order (EE/O) values of 5 kWh/m3 were measured for benzotriazole within the batch reactor. The batch reactor also reached the lowest mean values (11.8–15.9 kWh/m3) followed by the flat cell reactor (21.0–37.0 kWh/m3) and immersion rotary body reactor (23.9–41.0 kWh/m3). Catalyst arrangement and irradiation power were identified as major influences on the energetic performance of the reactors. Low radiation intensities as well as the use of submerged catalyst arrangement allowed a reduction in energy demand by a factor of 3–4. A treatment according to existing treatment goals of wastewater treatment plants (80% total degradation) was achieved using the batch reactor with a calculated energy demand of 7000 Wh/m3.
Operator Calculus Approach to Comparison of Elasticity Models for Modelling of Masonry Structures
(2022)
The solution of any engineering problem starts with a modelling process aimed at formulating a mathematical model, which must describe the problem under consideration with sufficient precision. Because of heterogeneity of modern engineering applications, mathematical modelling scatters nowadays from incredibly precise micro- and even nano-modelling of materials to macro-modelling, which is more appropriate for practical engineering computations. In the field of masonry structures, a macro-model of the material can be constructed based on various elasticity theories, such as classical elasticity, micropolar elasticity and Cosserat elasticity. Evidently, a different macro-behaviour is expected depending on the specific theory used in the background. Although there have been several theoretical studies of different elasticity theories in recent years, there is still a lack of understanding of how modelling assumptions of different elasticity theories influence the modelling results of masonry structures. Therefore, a rigorous approach to comparison of different three-dimensional elasticity models based on quaternionic operator calculus is proposed in this paper. In this way, three elasticity models are described and spatial boundary value problems for these models are discussed. In particular, explicit representation formulae for their solutions are constructed. After that, by using these representation formulae, explicit estimates for the solutions obtained by different elasticity theories are obtained. Finally, several numerical examples are presented, which indicate a practical difference in the solutions.