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In spite of the names of “building attachments” and “secondary elements” usually given to nonstructural components and equipment, they are far from being secondary in importance. In the worst case of critical facilities, the failure of equipment strongly impacts on the postearthquake functionality, causing the loss of essential services or businesses. Such considerations highlight the need for a seismic qualification of equipment as the essential means to demonstrate its adequacy to perform the required function during the expected earthquake event. In this paper, the authors deal with a new approach to the problem of seismic qualification, in which the seismic demand posed to the component is defined in terms of equipment seismic levels (ESLs), whereas its inherent seismic capacity is classified in terms of equipment qualification categories. The determination of the ESL, which is the subject of this paper, has to satisfy the requirements prescribed by the codes for the seismic design of equipment. A comparative study of the current seismic codes in the USA and in Europe is hence carried out in order to critically assess their provisions and to develop a methodology that can be implemented straightforwardly also by designers not having expertise in the field of earthquake engineering. [ABSTRACT FROM PUBLISHER]

Virtual powertrain analysis is widely applied in the automotive industry to cope with the increasing complexity and variance of future vehicle propulsion technologies. Since the vehicle-usage behavior has a strong impact on component loads, realistic computation results require accurate assumptions about these boundaries. In this context, driving cycles (DCs) are used to represent the system boundaries in vehicle operation. The aim of this article is to identify multiple characteristic driving cycles (CDCs) from extensive vehicle measurement data which represent the full variety of possible real-driving scenarios. Vehicle measurements are segmented and consumption-relevant features are extracted from each segment. These features are then used to apply clustering and classification techniques to identify characteristic groups that are consequently assigned to different driving environments and driving styles. In order to obtain even more realistic driving scenarios, a data-fusion approach is used to incorporate a road slope signal from a NASA digital elevation model for each segment. Lastly, a genetic mixed-integer optimization algorithm is proposed to efficiently generate representative DCs for each characteristic group of driving segments. The main contribution of this article is a data-driven identification of the parameter space of real-driving scenarios from extensive vehicle measurements including the implementation of road slope information. The scenarios are represented via a constrained number of compact CDCs which enables comprehensive investigations of new powertrain technologies under average as well as extreme real-driving conditions to develop efficiency-robust powertrain systems. [ABSTRACT FROM AUTHOR]