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This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, and a number of other related fields. The course derived from this strategy includes four parts: (a) Computing with a selected set of modern paradigms—the stress is on Control Flow and Data Flow computing paradigms, but paradigms conditionally referred to as Energy Flow and Diffusion Flow are also covered; (b) Project management that is holistic—the stress is on the wide plethora of issues spanning from the preparation of project proposals, all the way to incorporation activities to follow after the completion of a successful project; (c) Examples from past research and development experiences—the stress is on experiences of leading experts from academia and industry; (d) Student projects that stimulate creativity—the stress is on methods that educators could use to induce and accelerate the creativity of students in general. Finally, the article ends with selected pearls of wisdom that could be treated as suggestions for further elaboration.

This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).

Cities in seismic regions often have unknown numbers of older buildings vulnerable to strong earthquake ground motion. Simple methods are needed with urgency to screen these buildings. The problem is of immense proportions as it affects some of the largest cities in the world. Seismic vulnerability indices proposed by Hassan and Sozen (ACI Struct J 94(1):31–39, 1997), Islam et al. (Architectural Institute of Japan-Academic Lecture Collection Summary, 2017), NCREE (Chiou et al. in 16th World Conference on Earthquake Engineering, 16WCEE 2017, Santiago, 2017), and Shiga et al. (Proceedings, Tohuku District symposium, Architectural Institute of Japan, 1968) possess the required simplicity. The indices have different forms and have been calibrated through independent activities, but all imply that the key to building survival is in the relative sizes of elements resisting lateral inertial forces. Data collected after the 2016 Meinong, Taiwan, Earthquake were used to evaluate these indices. The evaluation showed that differences among indices did not lead to wide differences in the cost associated with a given benefit especially if they are used to include the effects of all lateral-load resisting elements including masonry infill.

Correlating damage level and changes in dynamic characteristics of a structure forms the basis for damage detection techniques in structural health monitoring. In reinforced concrete building structures such correlation is not well established. A damage detection technique capable of identifying the structural condition of the system based on its small amplitude vibration response is desirable because such response is easier to obtain. It is a common practice in engineering applications to estimate dynamic parameters from small-amplitude vibrations assuming a linear behavior of the structure. This simplification causes inaccurate estimation of the dynamic properties in reinforced concrete structures due to the presence of nonlinear elastic behavior. In this study no such assumption is made and a linear model is only used for sets of data corresponding to the same displacement amplitude of a nonlinear elastic system. The trends found between small-amplitude vibration dynamic properties and past levels of maximum displacement in various reinforced concrete structures are reported. In addition to analytical and numerical studies, results from a series of laboratory tests are reported to demonstrate the use of the approach. One full-scale three-story reinforced concrete flat-plate building and six small-scale reinforced concrete beams were examined. In this study, small displacements are defined as displacements below an overall drift ratio of 0.03%. The displacement dependence of the dynamic properties is considered explicitly. It was found that while fundamental frequencies of the examined reinforced concrete specimens were found to decrease uniformly as past peak displacement level increased, the equivalent viscous damping ratio was found to increase until the past peak displacement reached the neighborhood of nominal yield displacement and then observed to decrease when the specimens are pushed beyond the nominal yield displacement level, which has not been reported in literature before. Recommendations are provided as to how small amplitude vibration tests should be set up to avoid misleading observations due to nonlinear response at small amplitude response, observations that could lead to erroneous conclusions regarding the damage state of a structure.