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Seismic behavior of an existing unreinforced masonry building built pre-modern code, located in the City of Ohrid, Republic of North Macedonia has been investigated in this paper. The analyzed school building is selected as an archetype in an ongoing project named “Seismic vulnerability assessment of existing masonry structures in Republic of North Macedonia (SeismoWall)”. Two independent segments were included in this research: Seismic hazard assessment by creating a cite specific response spectra and Seismic vulnerability definition by creating a region - specific series of vulnerability curves for the chosen building topology. A reliable Seismic Hazard Assessment for a selected region is a crucial point for performing a seismic risk analysis of a characteristic building class. In that manner, a scenario – based method that incorporates together the knowledge of tectonic style of the considered region, the active fault characterization, the earth crust model and the historical seismicity named Neo Deterministic approach is used for calculation of the response spectra for the location of the building. Variations of the rupturing process are taken into account in the nucleation point of the rupture, in the rupture velocity pattern and in the istribution of the slip on the fault. The results obtained from the multiple scenarios are obtained as an envelope of the response spectra computed for the cite using the procedure Maximum Credible Seismic Input (MCSI). Capacity of the selected building has been determined by using nonlinear static analysis. MINEA software (SDA Engineering) was used for verification of the structural safety of the chosen unreinforced masonry structure. In the process of optimization of the number of samples, computational cost required in a Monte Carlo simulation is significantly reduced since the simulation is performed on a polynomial response surface function for prediction of the structural response. Performance point, found as the intersection of the capacity of the building and the spectra used, is chosen as a response parameter. Five levels of damage limit states based on the capacity curve of the building are defined in dependency on the yield displacement and the maximum displacement. Maximum likelihood estimation procedure is utilized in the process of vulnerability curves determination. As a result, region specific series of vulnerability curves for the chosen type of masonry structures are defined. The obtained probabilities of exceedance a specific damage states as a result from vulnerability curves are compared with the observed damages happened after the earthquake in July 2017 in the City of Ohrid, North Macedonia.
Additive Manufacturing (AM) of metallic workpieces faces a continuously rising technological relevance and market size. Producing complex or highly strained unique workpieces is a significant field of application, making AM highly relevant for tool components. Its successful economic application requires systematic workpiece based decisions and optimizations. Considering geometric and technological requirements as well as the necessary post-processing makes deciding effortful and requires in-depth knowledge. As design is usually adjusted to established manufacturing, associated technological and strategic potentials are often neglected. To embed AM in a future proof industrial environment, software-based self-learning tools are necessary. Integrated into production planning, they enable companies to unlock the potentials of AM efficiently. This paper presents an appropriate methodology for the analysis of process-specific AM-eligibility and optimization potential, added up by concrete optimization proposals. For an integrated workpiece characterization, proven methods are enlarged by tooling-specific figures.
The first stage of the approach specifies the model’s initialization. A learning set of tooling components is described using the developed key figure system. Based on this, a set of applicable rules for workpiece-specific result determination is generated through clustering and expert evaluation. Within the following application stage, strategic orientation is quantified and workpieces of interest are described using the developed key figures. Subsequently, the retrieved information is used for automatically generating specific recommendations relying on the generated ruleset of stage one. Finally, actual experiences regarding the recommendations are gathered within stage three. Statistic learning transfers those to the generated ruleset leading to a continuously deepening knowledge base. This process enables a steady improvement in output quality.
Research collaborations provide opportunities for both practitioners and researchers: practitioners need solutions for difficult business challenges and researchers are looking for hard problems to solve and publish. Nevertheless, research collaborations carry the risk that practitioners focus on quick solutions too much and that researchers tackle theoretical problems, resulting in products which do not fulfill the project requirements.
In this paper we introduce an approach extending the ideas of agile and lean software development. It helps practitioners and researchers keep track of their common research collaboration goal: a scientifically enriched software product which fulfills the needs of the practitioner’s business model.
This approach gives first-class status to application-oriented metrics that measure progress and success of a research collaboration continuously. Those metrics are derived from the collaboration requirements and help to focus on a commonly defined goal.
An appropriate tool set evaluates and visualizes those metrics with minimal effort, and all participants will be pushed to focus on their tasks with appropriate effort. Thus project status, challenges and progress are transparent to all research collaboration members at any time.