1,721,083 research outputs found
Innovative Approaches in Computational Structural Engineering
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Innovative Approaches in Computational Structural Engineering
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Analysis of structural effects due to ice formation inside hollow steel foundations
Full text linkMaster i bygg og- konstruksjonsteknikk - sivilingeniørThis master thesis is written on behalf of Statnett SF, which is the system operator of the Norwegian power system. Statnett has developed a new type of foundation which requires less time to assemble and is more convenient to transport than a typical concrete foundation. The foundation consists of a tubular section mounted on top of a steel pad. In soils with high water content, water fills the tubular section and freezes inside when the temperature drops below 0 (°C). The thesis investigates if the water that freezes inside the tubular section of the foundation has a damaging effect. The theory of heat transfer, the behavior and the properties of ice were studied. Based on the theory, a set of analyses and calculations were conducted. The analysis revealed the amount of water that can freeze inside the tubular section and the strength capacity of the section. The conclusion based on the research and the analysis results indicates that the force exerted by ice is within the strength capacity of the foundation.publishedVersio
Analysis of structural effects due to ice formation inside hollow steel foundations
No full textThis master thesis is written on behalf of Statnett SF, which is the system operator of the Norwegian power system. Statnett has developed a new type of foundation which requires less time to assemble and is more convenient to transport than a typical concrete foundation. The foundation consists of a tubular section mounted on top of a steel pad. In soils with high water content, water fills the tubular section and freezes inside when the temperature drops below 0 (°C). The thesis investigates if the water that freezes inside the tubular section of the foundation has a damaging effect. The theory of heat transfer, the behavior and the properties of ice were studied. Based on the theory, a set of analyses and calculations were conducted. The analysis revealed the amount of water that can freeze inside the tubular section and the strength capacity of the section. The conclusion based on the research and the analysis results indicates that the force exerted by ice is within the strength capacity of the foundation
Multi-objective optimization under uncertainly with real-time integrated decision making applied to structural engineering
No full textOne of the major tasks of structural engineering design optimization is the handling of uncertainties (such as variations in material properties, loading conditions, unknown environmental conditions or even uncertainties in modeling assumptions), which affect system performance in terms of robustness and reliability (or, in other words, the ability to respond to input variations with minimal alteration, loss of functionality or damage). This task is usually tackled with Optimization Under Uncertainty (OUU) methods[1], like robust design optimization and reliability-based design optimization. In most cases, the optimization has to deal with
multi-objective problems (such as maximizing the performance while minimizing costs, system response variations, etc). These problems do not have a unique solution, but a set of tradeoff optimal solutions (the so-called Pareto front). The action of a decision maker (DM) is necessary for choosing the final optimal design according to some (pre-defined) preferences or criteria. Multi-Criteria Decision Making (MCDM) techniques[2] have been developed over the past years to try to make these choices objective and rational. In most MCDM methods, the preferences are usually taken into account during some a-posteriori analyses of the optimization outcomes. Here we address both OUU and MCDM problems with an approach that integrates directly the action of the DM with the optimization process. The DM is asked to express their preferences (based on their previous experience) to drive the optimization towards the most preferred regions of the Pareto front. This can lead to a more efficient exploration of specific regions of the Pareto front and reduce the computational cost of finding desirable solutions. Interactive MCDM approaches have been recently given more attention in the multi-objective optimization community [3, 4, 5]. A validation of this approach on simple test-cases is shown as well as its application to the design of a simple building structure under uncertainties with seismic hazard and snow loads
Review and design of structural fiber reinforced concrete elements
Full text linkThis thesis reports main issues, and findings in designing FRC structural elements and presents a new approach for predicting the real residual flexural behavior of FRC elements validated using non-linear finite-element analysis and existing experimental data.
The literature survey prior to the work using systematic search and search analysis helped to identify the major gaps in the field of FRC structural element design and crystalized the idea of the need for new method and approaches.
The motivation of this work is to represent the real post-cracking flexural behavior of FRC elements as it is yet not represented well in fib Model Code guidelines; and to make the structural use of FRC material as the ONLY reinforcement in linear structural elements true, as well as to present a suitable analytical and numerical model for general use in analysis and design for FRC structural elements.
The main conclusion of this study is that the presented new approach is reliable and capable to represent the real flexural response of FRC elements in the scope of this work and unveil the limitations of fib Model Code in this regardpublishedVersio
Classification of wood defects using digital image processing and CNN models
Full text linkWood is a fundamental material in construction, but inherent surface imperfections such as knots, cracks, and resin pockets adversely affect its structural integrity, visual appeal, and reliability in grading. This study investigated the application of image processing on pre-trained convolutional neural networks (CNNs) for the classification of surface-level wood defects using image data. A total of 15 CNN architectures were evaluated for their ability to detect and classify eight defect types and a non-defective surface. The eight defect classes include surfaces with single defects such as a live-knot, dead-knot, missing-knot, knot-with-crack, resin, quartzite, marrow, and crack. A dataset encompassing 1310 images was distributed across the nine classes, with 60% of each category allocated for training and 40% for testing. The models were tested under multiple setups including multi-class, binary and pairwise classification, using both balanced and unbalanced datasets. MobileNetV2, EfficientNetB0 and Xception emerged as the top-performing models, with EfficientNetB0 achieving the highest F1-score at 76% in the full multi-class test with the unbalanced dataset and over 90% accuracy for defect categories such as “Cracked” surface and “Knot-with-crack. " The study found that CNNs perform best when trained and tested on larger datasets with visually distinct defects. However, the performance of pretrained models declines when detecting rare defects and visually overlapping defect categories. The findings highlight CNNs’ potential to improve grading consistency, reduce manual error, and increase efficiency in timber processing.
Keywords: Wood defects; CNNs; Image processing; Multi-class classification; Machine learning
Vulnerability Assessment of Existing Buildings and Structures
Full text linkVulnerability assessment of cultural heritage assets and unreinforced masonry buildings is important for providing a resilient framework and sustainable reconstruction proposals. Owing to the vulnerability of unreinforced masonry structures to earthquakes, different seismic vulnerability assessment methodologies have been developed and can be classified, based on the scale of application, into: 1) single structure scale 2) building stock scale, and 3) large scale. The thesis was mainly aimed at improving the current methodologies for seismic vulnerability assessment of historical constructions and proposing efficient methods using modern technologies. First, topics related to simplified analytical methods for large-scale seismic vulnerability assessments were reviewed and investigated. Simplified analytical methods are presented for calculating the initial in-plane and maximum lateral strengths of unreinforced masonry walls with openings. The seismic vulnerability of unreinforced masonry buildings at a single-structure scale was investigated using equivalent frame methods. A new macroelement and an open-source graphical user interface were developed. The efficiencies of the different equivalent frame methods were investigated by comparing the results of the nonlinear analysis of various case studies. Moreover, the effect of pulse-like near-field ground motions on the seismic behavior of low-rise unreinforced masonry buildings was evaluated using the proposed macroelement. Finally, the seismic vulnerability assessment of cultural heritage assets, at a single structure scale, using the continuum homogeneous method, was evaluated by emphasizing model calibration based on operational modal analysis and the effect of soil-structure interaction. Two methodologies were proposed for deriving the simulation-based digital twins of historic structures and applied to two case studies. The application of different optimal sensor placement techniques for detecting the optimized location of accelerometer sensors for ambient vibration testing was explored. Furthermore, the effect of pulse-like near-field excitations on the seismic behavior of a masonry arch bridge was studied.
Sårbarhetsvurdering av kulturminner og uarmerte murbygninger som er utbredt i historiske områder er viktig for å gi et robust rammeverk og forslag til bærekraftig gjenoppbygging. På grunn av sårbarheten til uarmerte mur konstruksjoner for jordskjelv, ble forskjellige metoder for vurdering av seismisk sårbarhet utviklet og kan klassifiseres basert på skalaen til applikasjonen i 1-enkeltstrukturskala 2- bygningsmasseskala og 3- storskala. Hovedmålet med denne doktorgradsavhandlingen er å forbedre dagens metodikk for seismisk sårbarhetsvurdering av historiske konstruksjoner og foreslå effektive metoder ved bruk av moderne teknologi. For det første er temaene knyttet til de forenklede analysemetodene for seismisk sårbarhetsvurdering i stor skala gjennomgått og undersøkt. Det ble presentert forenklede analytiske metoder for å beregne den innledende i-planet og maksimal side styrke til uarmerte murvegger med åpninger. Seismisk sårbarhet av uarmerte murbygninger i en enkelt strukturskala er undersøkt ved bruk av tilsvarende rammemetoder. Et nytt makroelement ble utviklet, og et grafisk brukergrensesnitt med åpen kildekode ble laget. Effektiviteten til ulike ekvivalente rammemetoder har blitt undersøkt ved å sammenligne resultatene fra den ikke-lineære analysen av ulike casestudier. Dessuten ble effekten av pulslignende nærfelt-bakkebevegelser på den seismiske oppførselen til lave, uarmerte murbygninger evaluert ved hjelp av det foreslåtte makroelementet. Til slutt ble seismisk sårbarhetsvurdering av kulturminner i en enkelt strukturskala ved bruk av kontinuum homogen metode evaluert ved å vektlegge modellkalibrering basert på operasjonell modal analyse og effekten av jord-struktur interaksjon. To metoder for å utlede de simuleringsbaserte digitale tvillingene til historiske konstruksjoner ble foreslått og brukt på to casestudier. Anvendelsen av forskjellige optimale sensorplasseringsteknikker for å oppdage den optimaliserte plasseringen av akselerometersensorene for å utføre omgivelsesvibrasjonstesting ble utforsket. Videre ble effekten av pulslignende nærfelteksitasjoner på den seismiske oppførselen til en mur buebro studert.publishedVersio
Structural damage localization and quantification in plane truss structures using optimization techniques
Full text linkCivil infrastructures are susceptible to threats from both nature and human activity; as they are built and used, they deteriorate, potentially resulting in structural damage or even collapse. The detection of structural damage is an important field of study that aims to identify and quantify any possible damage to structures such as bridges, buildings, and other infrastructure. Early detection of structural deterioration benefits the identification of cracks, flaws, and other possible safety issues in civil infrastructure. Identifying and quantifying structural damage with methods based on dynamic analysis data of structures is the main objective of the present study.
The damage identification problem is approached as an optimization problem, which is solved using two optimization techniques: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Three objective functions based on dynamic analysis data of the structures such as modal flexibilities, natural frequencies, and mode shapes are used in the optimization process. This data was gathered by developing a program that performs the dynamic analysis of structures using the Finite Element Method (FEM). The effectiveness of each objective function is assessed through evaluations conducted on three damage scenarios involving a 10-bar truss structure. The impacts of noise and damage levels on damage detection are investigated.publishedVersio
Reliability Analysis of RC Code for Predicting Load-Carrying Capacity of RCC Walls Through ANN
No full textOver the past couple of decades, a significant rise in utilization of artificial neural network (ANN) in the field of civil engineering has been observed. ANNs have been proven to be very helpful for researchers working in concrete technology. Reinforced cement concrete (RCC) shear walls play an important role in the stability of high-rise reinforced concrete structures. Current study is focused on using ANN-based design technique as an alternative to conventional design codes and physical models to estimate the ultimate load carrying capacity of RCC shear walls. In this study, database of 95 RCC wall samples has been collected from previously published literature. Various critical parameters considered for current research are; length of web portion of the wall (Lw), thickness of wall boundary member (bw), effective depth of wall (d), height of wall (H), shear span ratio (av/d), vertical steel ratio (ρv), horizontal steel ratio (ρh), yield strength of vertical and horizontal steel (fy), compressive strength of concrete (fc), and the ultimate load carrying capacity (Vexp)
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