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.…).

City centres of Europe are often composed of unreinforced masonry structural aggregates, whose seismic response is challenging to predict. To advance the state of the art on the seismic response of these aggregates, the Adjacent Interacting Masonry Structures (AIMS) subproject from Horizon 2020 project Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe (SERA) provides shake-table test data of a two-unit, double-leaf stone masonry aggregate subjected to two horizontal components of dynamic excitation. A blind prediction was organized with participants from academia and industry to test modelling approaches and assumptions and to learn about the extent of uncertainty in modelling for such masonry aggregates. The participants were provided with the full set of material and geometrical data, construction details and original seismic input and asked to predict prior to the test the expected seismic response in terms of damage mechanisms, base-shear forces, and roof displacements. The modelling approaches used differ significantly in the level of detail and the modelling assumptions. This paper provides an overview of the adopted modelling approaches and their subsequent predictions. It further discusses the range of assumptions made when modelling masonry walls, floors and connections, and aims at discovering how the common solutions regarding modelling masonry in general, and masonry aggregates in particular, affect the results. The results are evaluated both in terms of damage mechanisms, base shear forces, displacements and interface openings in both directions, and then compared with the experimental results. The modelling approaches featuring Discrete Element Method (DEM) led to the best predictions in terms of displacements, while a submission using rigid block limit analysis led to the best prediction in terms of damage mechanisms. Large coefficients of variation of predicted displacements and general underestimation of displacements in comparison with experimental results, except for DEM models, highlight the need for further consensus building on suitable modelling assumptions for such masonry aggregates.

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.

In this study, two machine-learning algorithms based on the artificial neural network (ANN) model are proposed to estimate the ultimate compressive strength of square concrete-filled steel tubular columns. The development of such prognostic models is achievable since an extensive set of experimental tests exist for these members. The models are developed to use the simplest possible network architecture but attain very high accuracy. A total dataset of 1022 specimens with 685 stub columns and 337 slender columns subjected to pure axial compression is collected from the available literature. This is significant for the development of the initial model considering that for this field it falls under the scope of big data analysis. The ANN models are validated by comparison with experimental results. The validation study has shown the superiority of surrogate models over the Eurocode 4 design code. The empirical equation derived from the best-tuned Bayesian regularization algorithm shows a better agreement with the experimental results than those obtained by the Levenberg–Marquardt algorithm, and Eurocode 4 design code. A similar conclusion applies to stub and slender columns independently. The Bayesian regularization-based model is negligibly slower than the one developed on the Levenberg–Marquardt algorithm but gives a better generalization even with simplified ANN. Generally, besides its high accuracy, one of the key benefits of the presented ANN model is its applicability to a broader range of columns than Eurocode 4 and other studies.

Seismic risk assessment in school buildings is critical for ensuring the safety and resilience of educational institutions against seismic events. This paper presents a new seismic risk methodology named MM Risk and comparative study with Adriseismic methodology used for seismic risk assessment. The study aims to provide insights into the effectiveness and reliability of these methods in evaluating the seismic vulnerability of school buildings. Through a comprehensive review of the existing literature and application on a dataset of 213 schools (367 buildings), this paper evaluates the strengths and limitations of each method in terms of accuracy, complexity, and practical applicability. The results show that by integrating the approach of Adriseismic methodology and incorporating extensions related to irregularities, the social risk component (number of users), and the ability to assess different types of structures, a comprehensive and tailored methodology for assessing seismic risk can be developed. This is important since these factors are strongly influencing the seismic risk of schools as connected systems. Furthermore, this paper explores the implications of these findings for improving seismic risk mitigation strategies in school buildings. MM Risk methodology places over 70% of school buildings in the medium seismic risk category and 27% in the high seismic risk category. On the other hand, the Adriseismic methodology is more stringent, classifying 60% of school buildings into high and very-high risk categories. This disparity undoubtedly influences the prioritization list for seismic risk mitigation measures. However, definitely the comparative analysis presented in this paper offers valuable guidance for engineers, policymakers, and stakeholders involved in the seismic retrofitting and design of school buildings, ultimately contributing to the enhancement of seismic resilience in educational infrastructure.

The fundamental period (TFP) of vibration is the time required for a structure to complete one full cycle of vibration, and it is one of the main features of the structural system. It highly influences the behavor of structures under earthquake excitation. Therefore, its determination is of highest importance for the seismic design of buildings. This is a complex task in a case of masonry infilled RC frames, due to high stiffness of infill walls and their interaction with the surrounding frame. In this study, using Artificial Neural Network (ANN) algorithms, the fundamental (natural) period of vibration of fully or partially masonry infilled Reinforced Concrete (RC) frame structures was evaluated. Accurate prediction of TFP has a key role in ensuring the safety and resilience of structures, which is particularly expressed in seismic-prone regions. The collected database from open literature contains 4026 samples which cover a wide range of RC frames. For practical reasons, the database is divided in the preprocessing phase into two parts that contain either bare frames or fully/partially infilled frames. The authors compared first and second-order ANN paradigms, and conducted feature importance analysis. The proposed models were validated and verified by comparison with the experimental data, seismic design codes, and derived equations by other researchers. In addition to the superiority that the BRA model has shown in comparison with other solutions, it also enables simpler approach for the calculation of TFP, unlike the proposals of other authors. Based on the results from best ANN model, multi-objective optimization genetic algorithm was employed for additional optimization of TFP which include investigation of the most optimal solutions between two conflicting objective functions.