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To increase the pace of the design of safer road infrastructure and raise the active and passive safety features of road structures on the global stage, innovative and smart virtual tools are essential. One of the basic steps for such ground breaking numerical simulation technology would be to develop advanced smart hybrid techniques with dynamic adaptation into mainstream design and simulation tools that are used by engineering offices. In the research work herein, a new numerical framework including dynamic zoning, advanced grid interfacing, new computationally-efficient solvers, and genetic algorithm symbolic-regression has briefly been presented to address long-standing problems of speed, accuracy, and reliability of numerical tools. The fundamental physical and mathematical aspects of the new simulation framework are concisely presented. In addition, some outcomes of real-world case studies utilized using the proposed hybrid analytical and data-driven (i.e., machine learning, ML) scheme have been shown, where the design rule for road gantry structures is interrogated using the developed virtual tool. One of the main contributions of this paper is to show the benefits of using hybrid simulation technologies to model engineering systems along with the ML-based method to optimize their designs.

Europe has set a target to become a decarbonised continent by 2050. To achieve this, intervention in buildings is crucial, as they serve as significant energy consumers and greenhouse gas emitters. This intervention encompasses two essential pathways: renovation and digitalisation. The combination of these two aspects gives rise to elements such as the Digital Building Logbook (DBL), a digital data repository expected to enhance the pace and quality of renovation efforts. This paper introduces, for the first time, a European DBL data model with a specific focus on building renovation purposes—the DBrL. It outlines its initial requirements, constituent entities, relationships, and attributes. While acknowledging the need to address issues related to data protection, integration with existing data sources, and connections with Building Information Modelling (BIM) and Geographic Information System (GIS) in subsequent design phases, the study’s outcome represents a significant stride in defining this tool.

Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation (DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory (Bi-LSTM) based quasi-static magnetic field (QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.

Output is compared from four methods used to estimate the overtopping rate at seawalls subject to random wave action: two empirical models, a semiempirical model, and a numerical model. The empirical models were developed by fitting dimensionless groups to data derived from physical model tests. The semiempirical model was derived from consideration of the unsteady flow of water over a weir. However, like the empirical models, it was calibrated with the results of physical model tests. In contrast, the numerical model AMAZON is a high-resolution two-dimensional finite volume model based on the nonlinear shallow-water equations. In this study, we calculated the mean overtopping discharge for a range of seawalls with front slopes from 1 : 1 to 1 : 20 and for incident wave steepnesses from 0.01 to 0.03. The results are considered alongside four sets of data from physical model tests. They show general agreement between the output from the numerical and semiempirical models and the data. Agreement with the empirical models depends principally on the value of the surf similarity parameter. The empirical models substantially overpredict discharges for some conditions.

This paper illustrates the application of the new version of a nonlinear shallow water numerical model, AMAZON, to study the mean wave overtopping discharge at a porous breakwater that protects the Portuguese harbor of Póvoa de Varzim. The results are compared with two-dimensional physical model data collected at the National Civil Engineering Laboratory, Portugal. The implications of using different porous flow parameters in the Forchheimer equation for stationary turbulent flow within the porous layer of the breakwater are discussed. The maximum velocity that the water can reach during the exchange between the free-flow and porous layers has been included as an input to AMAZON and its impact on the overtopping results is analyzed. A suitable choice of the values of the porous flow parameters and of the maximum velocity leads to a good agreement between the AMAZON results and the data. The specified maximum velocity was found to be the parameter which mostly affects the obtained results.

The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloud–shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are both conceptual and pragmatic in the definition of remote sensing best practices, which is the focus of efforts made by intergovernmental organizations such as the Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS). First, the ESA EO Level 2 product definition is recommended for consideration as state-of-the-art EO Analysis Ready Data (ARD) format. Second, systematic multi-sensor ESA EO Level 2 information product generation is regarded as: (a) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (b) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2 (proposed as Supplementary Materials), the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed.