Δημοσιεύσεις | RDC Informatics

Abstract: Heavy-duty construction machinery together with all the diesel engine-based vehicles, as those used for logistics and transportation, are of paramount importance for the economy in both developed and under development societies. Despite the strong interest in e-mobility recently, it is practically very difficult to replace diesel engine-based vehicles by electrical alternatives specifically for application domains associated with logistics and heavy construction works. Traditionally, these vehicles are characterized by high costs of investment and ownership, expensive repair and maintenance services and requirement for high operational availability. This paper presents the design of a portable that allows the diagnosis and debugging of electronic control units of transmission systems as well as diesel engines. Amongst the most advantages of this device are the capability of effective service in-situ and its versatile support of all the existing commercial transmissions and diesel engines. Moreover, the total repair cost and time are significantly decreased.

Abstract: In this review paper, we computationally analyze a vast volume of published articles in the field of Adaptive Learning, as obtained by the Scopus Database. Particularly, we use a query with search terms targeting the area of Adaptive Learning Systems by utilizing a combination of specific keywords. Accordingly, we apply a multidimensional scaling algorithm to construct bibliometric maps for keywords, authors, and references. Subsequently, we present the computational results for the studied dataset, reveal significant patterns appearing in the field of adaptive learning and the inter-item associations, and interpret the findings based on the current state-of-the-art literature in the area. Furthermore, we demonstrate the time-series of the evolution of the research terms, their trends over time, as well as their prevalent statistical associations.

Abstract: The landscape of structural design, which aims to develop safe and sustainable structures, has been formed by applying a specific methodology that foresees the use of empirical and semi-empirical knowledge. This methodology studies experimental data and observations in an attempt to develop design formulae.

Abstract: The use of curved I-beams has been increasing throughout the years as the steel forming industry continues to advance. However, there are often design limitations on such structures due to the lack of recommendations and design code formulae for the estimation of the expected deflection of these structures. This is attributed to the lack of understanding of the behaviour of curved I-beams that exhibit extreme torsion and bending. Thus, currently, there are no formulae readily available for practising engineers to use to estimate the deflection of curved beams. Since the design of light steel structures is often governed by serviceability considerations, this paper aims to analyse the properties of curved steel I-beams and their impact on deflection as well as develop an accurate formula that will be able to predict the expected deflection of these beams. By using a combination of an experimentally validated finite element modelling approach and machine learning. Numerous formulae are developed and tested for the needs of this research work. The final proposed formula, which is the first of its kind, was found to have an average error of 4.11% in estimating the midspan deflection on the test dataset.

Abstract: With the development of technology and building materials, the world is moving towards creating a better and safer environment. One of the main challenges for reinforced concrete structures is the capability to withstand the seismic loads produced by earthquake excitations, through using the fundamental period of the structure. However, it is well documented that the current design formulae fail to predict the natural frequency of the considered structures due to their inability to incorporate the soil-structure interaction and other features of the structures. This research work extends a dataset containing 475 modal analysis results developed through a previous research work. The extended dataset was then used to develop three predictive fundamental period formulae using a machine learning algorithm that utilizes a higher-order, nonlinear regression modelling framework. The predictive formulae were validated with 60 out-of-sample modal analysis results. The numerical findings concluded that the fundamental period formulae proposed in this study possess superior prediction ability, compared to all other international proposed formulae, for the under-studied types of buildings.

Abstract: Scientific literature is prosperously evolving, exhibiting exponential growth in the last decades. For a wide range of scientific thematic areas, it is hard or even impossible for individual researchers to analyze in detail the available published works. For this purpose, we utilize a robust multidimensional scaling procedure, to construct the bibliometric maps of the literature, for keywords, authors and references. Particularly, we propose a generic machine learning algorithm for multidimensional scaling and describe the algorithmic procedure for the generation of the bibliometric maps.

Abstract: Performance metrics (Evaluation metrics or error metrics) are crucial components of regression analysis and machine learning-based prediction models. A performance metric can be defined as a logical and mathematical construct designed to measure how close the predicted outcome is to the actual result. A variety of performance metrics have been described and proposed in the literature. Knowledge about the metrics’ properties needs to be systematized to simplify their design and use. In this work, we examine various regression related metrics (14 in total) for continuous variables, including the most widely used ones, such as the (root) mean squared error, the mean absolute error, the Pearson correlation coefficient, and the coefficient of determination, among many others. We provide their mathematical formulations, as well as a discussion on their use, their characteristics, advantages, disadvantages, and limitations, through theoretical analysis and a detailed numerical example. The 10 unitless metrics are further investigated through a numerical analysis with Monte Carlo Simulation based on (i) random guessing and (ii) the addition of random noise with various noise ratios to the predicted values. Some of the metrics show a poor or inconsistent performance, while others exhibit good performance as evaluation measures of the “goodness of fit”. We highlight the importance of the usage of the right metrics to obtain good predictions in machine learning and regression models in general.

Abstract: The fundamental period of buildings is an important parameter when designing seismic resistant structures. The current formulae proposed in design codes for determining the fundamental period of steel structures cannot accurately predict the fundamental period of real structures. In addition, most of the current formulae only consider the height of the structure in their formulation, while soil structure interaction (SSI) and the orientation of the I-columns that influence the fundamental period are usually neglected. This research focuses on the use of machine learning algorithms to obtain a new formula that accounts for different geometrical features of the superstructure, where the SSI effect is also considered. After training and testing a 40-feature formula, an additional 138 out-of-sample numerical results were used to further test the accuracy of the proposed formula’s prediction abilities. The validation resulted in a correlation of 99.71%, which suggests that the proposed formula exhibits high predictive features for the steel structures considered in this study.

Abstract: Calculating the shear capacity of slender reinforced concrete beams without shear reinforcement was the subject of numerous studies, where the eternal problem of developing a single relationship that will be able to predict the expected shear capacity is still present. Using experimental results to extrapolate formulae was so far the main approach for solving this problem, whereas in the last two decades different research studies attempted to use artificial intelligence algorithms and available data sets of experimentally tested beams to develop new models that would demonstrate improved prediction capabilities. Given the limited number of available experimental databases, these studies were numerically restrained, unable to holistically address this problem. In this manuscript, a new approach is proposed where a numerically generated database is used to train machine-learning algorithms and develop an improved model for predicting the shear capacity of slender concrete beams reinforced only with longitudinal rebars. Finally, the proposed predictive model was validated through the use of an available ACI database that was developed by using experimental results on physical reinforced concrete beam specimens without shear and compressive reinforcement. For the first time, a numerically generated database was used to train a model for computing the shear capacity of slender concrete beams without stirrups and was found to have improved predictive abilities compared to the corresponding ACI equations. According to the analysis performed in this research work, it is deemed necessary to further enrich the current numerically generated database with additional data to further improve the dataset used for training and extrapolation. Finally, future research work foresees the study of beams with stirrups and deep beams for the development of improved predictive models.

Abstract: Civil engineers around the world strive to construct a sustainable and safe built environment by engaging numerous design and numerical tools. The design and assessment of structures is performed through the use of national and international design codes that usually suggest the use of semi-empirical design formulae.