Συσχέτιση Σεισμικών Παραμέτρων και Ολικών Δεικτών Βλάβης σε Κατασκευές Οπλισμένου Σκυροδέματος

In the present work, a correlation is made between seismic intensity parameters and total damage indicators of reinforced concrete structures. For this purpose, natural ground motions recordings were used, from which intensity measures were calculated and alternatives were proposed. Then, through dynamic inelastic time history analyzes, damage indices were evaluated for 1st and 2nd order theory (P-Delta effects). The correlation between each seismic parameter and the corresponding damage index was made through polynomial regressions. Seismic parameters that do not take into account the characteristics of the building, showed a minimal or moderate correlation, while spectral parameters of velocities and energies explain the change of damage indices in percentages above 80 or even 90 per cent.Στην παρούσα εργασία πραγματοποιείται συσχέτιση μεταξύ σεισμικών παραμέτρων έντα-σης και ολικών δεικτών βλάβης σε κατασκευές οπλισμένου σκυροδέματος. Για αυτόν τον σκοπό, χρησιμοποιήθηκαν καταγραφές πραγματικών σεισμών, από τις οποίες υπολογίστηκαν παράμετροι, που χαρακτηρίζουν ένα επιταχυνσιογράφημα και προτάθηκαν εναλλακτικές τους. Στη συνέχεια μέσω δυναμικών ανελαστικών αναλύσεων χρονοϊστορίας αποτιμήθηκαν δείκτες βλάβης για θεωρία 1ης και 2ης τάξης. Η συσχέτιση μεταξύ του εκάστοτε ζεύγους σεισμικής παραμέτρου και δείκτη βλάβης έγινε μέσω πολυωνυμικών παλινδρομήσεων. Σεισμικές παρά-μετροι που δεν λαμβάνουν υπόψη τους τα χαρακτηριστικά του ταλαντωτή, παρουσίασαν ελάχιστη ή μέτρια συσχέτιση, ενώ φασματικές παράμετροι ταχυτήτων και ενεργειών ερμη-νεύουν τη μεταβολή των δεικτών βλάβης σε ποσοστά άνω του 80 ή και 90 τοις εκατό

Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application.Keywords: seismic sequence; machine learning algorithms; repeated earthquakes; structural damage prediction; intensity measures; damage accumulation; machine learning; artificial neural networ

Structural Damage Prediction Under Seismic Sequence Using Neural Networks

Advanced machine learning algorithms, such as neural networks, have the potential to be successfully applied to many areas of system modelling. Several studies have been already conducted on forecasting structural damage due to individual earthquakes, ignoring the influence of seismic sequences, using neural networks. In the present study, an ensemble neural network approach is applied to predict the final structural damage of an 8-storey reinforced concrete frame under real and artificial ground motion sequences. Successive earthquakes consisted of two seismic events are utilised. We considered 16 well-known ground motion intensity measures and the structural damage that occurred by the first earthquake as the features of the machine-learning problem, while the final structural damage was the target. After the first seismic events and after the seismic sequences, both actual values of damage indices are calculated through nonlinear time history analysis. The machine-learning model is trained using the dataset generated from artificial sequences. Finally, the predictive capacity of the fitted neural network is accessed using the natural seismic sequences as a test set

Position Paper on Water, Energy, Food and Ecosystem (WEFE) Nexus and Sustainable development Goals (SDGs)

The EU and the international community is realising that the Water, Energy, Food and Ecosystem components are interlinked and require a joint planning in order to meet the daunting global challenges related to Water, Energy and Food security and maintaining the ecosystem health and in this way, reach the SDGs. If not dealt with, the world will not be able to meet the demand for water, energy and food in a not too far future and, in any case, in a not sustainable way. The strain on the ecosystems resulting from unsustainable single-sector planning will lead to increasing poverty, inequality and instability. The Nexus approach is fully aligned with and supportive of the EU Consensus on Development. Key elements of the Consensus will require collaborative efforts across sectors in ways that can be supported/implemented by a Nexus approach. In this way, transparent and accountable decision-making, involving the civil society is key and common to the European Consensus on Development and the Nexus approach. The Nexus approach will support the implementation of the SDG in particular SDG 2 (Food), SDG 6 (Water) and SDG 7 (Energy), but most SDGs have elements that link to food, water and energy in one or other way, and will benefit from a Nexus approach. The SDGs are designed to be cross-cutting and be implemented together, which is also reflected in a WEFE Nexus approach. A Nexus approach offers a sustainable way of addressing the effects of Climate Change and increase resilience. The WEFE Nexus has in it the main drivers of climate change (water, energy and food security) and the main affected sectors (water and the environment). Decisions around policy, infrastructure, … developed based on the WEFE Nexus assessments will be suitable as elements of climate change mitigation and adaptation. In fact, it is difficult to imagine solutions to the climate change issue that are not built on a form of Nexus approach. The Nexus approach is being implemented around the world, as examples in the literature demonstrate. These examples together with more examples from EU and member state development cooperation will help build experience that can be consolidated and become an important contribution to a Toolkit for WEFE Nexus Implementation. From the expert discussions, it appears that because of the novelty of the approach, a Toolkit will be an important element in getting the Nexus approach widely used. This should build on experiences from practical examples of NEXUS projects or similar inter-sectorial collaboration projects; and, there are already policy, regulation and practical experience to allow institutions and countries to start applying the Nexus concept.JRC.D.2-Water and Marine Resource

Organic Fertilization and Tree Orchards

Organic fertilization has been proposed as an alternative approach to supply nutrients for crops, in the frame of organic and sustainable agriculture, with the aim to decrease high inorganic fertilization rates, protect the environment and decrease production costs for farmers. Since different types of organic fertilizers, such as manures, olive mill wastewater (OMW), sewage sludge (SS), crushed pruning wastes, composts and cover crops, exist as soil amendments to improve soil fertility, enhance plant nutrition and sustain the productivity of tree crops, their role as biofertilizers has been fully analyzed under the most important published papers. In addition, the benefits and drawbacks of organic fertilization, in a comparative approach with inorganic fertilization, are presented and discussed. Within the most important advantages of organic fertilizers, the enhancement of beneficial soil microorganisms and the improvement in soil physical properties and fertility should be included, while their most important disadvantage is their inability to directly satisfy the prompt N nutritional needs of tree crops, due to slow N mineralization rates. Finally, some novel aspects on the interrelation among innovative organic fertilizers for tree crops, sustainable field management, crop productivity and fruit quality are also included in this review, under the light of the most important and recent research data existing in the literature, with the aim to provide recommendations and future directions for organic fertilizers by tree growers

Structural Pounding Effect on the Seismic Performance of a Multistorey Reinforced Concrete Frame Structure

During intense ground motion excitations, the pounding between adjacent buildings may result in extensive structural damage. Despite the provision of regulations regarding the minimum separation gap required to prevent structural collisions, the majority of existing structures are poorly separated. The modern seismic design and assessment of structures are based on the definition of acceptable response levels in relation to the intensity of seismic action, which is usually determined by an acceptable probability of exceedance. From this point of view, the seismic performance of a typical eight-storey reinforced concrete (RC) frame structure is evaluated in terms of pounding. In particular, the performance is evaluated using six different separation gap distances as a fraction of the EC8 minimum distance. As the height of the adjacent structure affected the required separation distance, the examined RC structure was assumed to interact with four idealized rigid structures of one to four storeys. The typical storey height was equal between the examined structures; therefore, collision could occur at the diaphragm level. To this end, incidental dynamic analyses (IDAs) were performed, and the fragility curves for different limit states were obtained for each case. Finally, the seismic fragility was combined with the hazard data to evaluate the seismic performance probabilistically

Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application

Enhancing the Yield of Mature Olive Trees via Comparative Fertilization Strategies, including a Foliar Application with Fulvic and Humic Acids, in Non-Irrigated Orchards with Calcareous and Non-Calcareous Soils

This study investigated the impact of fertilization treatments on mature, forty-year-old, fully productive olive trees (Olea europaea L. cv. Koroneiki) in two non-irrigated orchards featuring contrasting soil types: non-calcareous–acidic and calcareous–alkaline. Over three years (2019–2021), seven distinct treatments were applied, involving inorganic soil fertilizers (traditional strategy) and foliar applications of a liquid organic product containing fulvic and humic acids. Fertilization significantly influenced physiological parameters, such as mineral nutrition, photosynthetic pigments of olive leaves, fruit yield, and oil production per tree, revealing noteworthy effects influenced by soil types and their interactions with treatments. Statistical analysis highlighted specific treatments, indicating that the foliar application of the organic product once a year in alkaline soil or biannually in acidic soil resulted in the highest observed fruit yield and oil production per tree. Indeed, contrary to the control–unfertilized trees, specific fertilization strategies resulted in an average increase of up to 47% and 73% in fruit yield per tree and up to 96% and 100% in oil production per tree in acidic and alkaline soil, respectively. Furthermore, consistently high yields were correlated with constant high values of the chlorophyll a/chlorophyll b ratio (2.3–3.3 in August for acidic soil) and leaf chlorophyll a concentration (55–66 mg/100 g f.w. in August for alkaline soil). This novel finding underscores the crucial role of these factors as prerequisites for achieving superior fruit production. Our results emphasize the potential of integrating foliar organic fertilization as a complementary strategy to traditional soil-based approaches. This is particularly relevant under non-irrigated/rainfed cultural conditions, emphasizing the significance of considering alternative fertilization practices for optimized olive orchard management