I was somewhat surprized with the fog in Groningen upon my arrival. This is notthe fog that covers the beautiful landscapes of the northern Netherlands in theevening and in the early morning. No… It is the fog that obscures the real aspectsof the earthquake problem in the region and is crystallised in the phrase “Groningen earthquakes are different”, which I have encountered numerous times whenever I raised a question of the type “But why..?”. A sentence taken out of the quiver as the absolute technical argument which mysteriously overshadows the whole earthquake discussion.Q: Why do we not use Eurocode 8 for seismic design, instead of NPR?A: Because the Groningen earthquakes are different!Q: Why do we not monitor our structures like the rest of the world does?A: Because the Groningen earthquakes are different!Q: Why does NPR, the Dutch seismic guidelines, dictate some unusual rules?A: Because the Groningen earthquakes are different!Q: Why are the hazard levels incredibly high, even higher than most Europeanseismic countries?A: Because the Groningen earthquakes are different!and so it keeps going…This statement is very common, but on the contrary, I have not seen a single piece of research that proves it or even discusses it. In essence, it would be a difficult task to prove that the Groningen earthquakes are different. In any case it barricades a healthy technical discussion because most of the times the arguments converge to one single statement, independent of the content of the discussion. This is the reason why our first research activities were dedicated to study if the Groningen earthquakes are really different. Up until today, we have not found any major differences between the Groningen induced seismicity events and natural seismic events with similar conditions (magnitude, distance, depth, soil etc…) that would affect the structures significantly in a different way.Since my arrival in Groningen, I have been amazed to learn how differently theearthquake issue has been treated in this part of the world. There will always bedifferences among different cultures, that is understandable. I have been exposed to several earthquake engineers from different countries, and I can expect a natural variation in opinions, approaches and definitions. But the feeling in Groningen is different. I soon realized that, due to several factors, a parallel path, which I call “an augmented reality” below, was created. What I mean by an augmented reality is a view of the real-world, whose elements are augmented and modified. In our example, I refer to the engineering concepts used for solving the earthquake problem, but in an augmented and modified way. This augmented reality is covered in the fog I described above. The whole thing is made so complicated that one is often tempted to rewind the tape to the hot August days of 2012, right after the Huizinge Earthquake, and replay it to today but this time by making the correct steps. We would wake up to a different Groningen today. I was instructed to keep the text as well as the inauguration speech as simple aspossible, and preferably, as non-technical as it goes. I thus listed the most common myths and fallacies I have faced since I arrived in Groningen. In this book and in the presentation, I may seem to take a critical view. This is because I try to tell a different part of the story, without repeating things that have already been said several times before. I think this is the very reason why my research group would like to make an effort in helping to solve the problem by providing different views. This book is one of such efforts.The quote given at the beginning of this book reads “How quick are we to learn: that is, to imitate what others have done or thought before. And how slow are we to understand: that is, to see the deeper connections.” is from Frits Zernike, the Nobel winning professor from the University of Groningen, who gave his name to the campus I work at. Applying this quotation to our problem would mean that we should learn from the seismic countries by imitating them, by using the existing state-of-the-art earthquake engineering knowledge, and by forgetting the dogma of “the Groningen earthquakes are different” at least for a while. We should then pass to the next level of looking deeperinto the Groningen earthquake problem for a better understanding, and alsodiscover the potential differences.
The maritime transport industry is facing a series of challenges due to the phasing out of fossil fuels and the challenges from decarbonization. The proposal of proper alternatives is not a straightforward process. While the current generation of ship design software offers results, there is a clear missed potential in new software technologies like machine learning and data science. This leads to the question: how can we use modern computational technologies like data analysis and machine learning to enhance the ship design process, considering the tools from the wider industry and the industry’s readiness to embrace new technologies and solutions? The obbjective of this PD project is to bridge the critical gap between the maritime industry's pressing need for innovative solutions for a more agile Ship Design Process; and the current limitations in software tools and methodologies available via the implementation into Ship Design specific software of the new generation of computational technologies available, as big data science and machine learning.
Digitalisering en dataficering zijn belangrijke ontwikkelingen die zich in ons dagelijkse leven voltrekken. Bedrijven worden steeds afhankelijker van data waarop ze hun bedrijfsprocessen en verdienmodellen baseren. Maar in hoeverre is die data volledig en betrouwbaar? Dataketens vormen het digitale fundament voor datagedreven organisaties om optimale bedrijfsprocessen, producten of diensten (servitization) te kunnen realiseren. De praktijk laat echter zien dat nog veel organisaties vaak te snel stappen zetten naar het ontwikkelen en implementeren van op data gebaseerde digitale oplossingen, zonder dat het ‘datafundament’ daarvoor op orde is. Dat leidt tot onbetrouwbare data en suboptimale besluitvorming. Data-engineering en -management in dataketens (DEMAND) realiseert een onderzoeksinfrastructuur die nieuwe oplossingen ontwikkelt voor geïntegreerde en gevalideerde dataketens van overheden en bedrijven. Het consortium heeft de overall ambitie om een toolbox te ontwikkelen met nieuwe toepasbare digitale technologieën en methodologieën die bijdragen aan optimalisatie van dataketens in private en publieke organisaties. Hierbij staan datagerichte activiteiten in dataketens van organisaties centraal, met inbegrip van data-acquisitie en data-opslag, data-(pre)processing, data-exploratie, data-modeling en data-benutting. Deze zogenaamde Key Enabling Methodologies verbinden mens en maatschappij langs de lat van Technical Readiness Levels en Societal Readiness Levels, zij vegroten het (toekomstig) verdienvermogen van Nederland en dragen bij aan maatschappelijke opgaven. Het DEMAND-programma wordt uitgevoerd door HAN University of Applied Sciences (HAN), Saxion Hogeschool en Fontys Hogeschool en dertien consortiumpartners met bewezen technische en organisatorische kennis van dataketens. DEMAND fungeert als bruggenbouwer tussen enerzijds de onderzoekspraktijk met de onderwijspraktijk en anderzijds de onderzoekspraktijk met het werkveld. Een set van casussen, de learning communities, vormen de basis van de onderzoeksinfrastructuur waarin de SPRONG-groep in co-creatie en interdisciplinair samenwerkt. Het consortium intensiveert de bestaande casussen én ontwikkelt daarnaast nieuwe casussen in data-intensieve omgevingen in het private als ook het publieke domein. Dit leidt tot meervoudige waardecreatie en systemische veranderingen (impact).
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
