An experimental and numerical investigation on strengthening the upright component of thin-walled cold-formed steel rack structures

Cold-formed steel (CFS) racking systems are widely used for storing products in warehouses. However, as commonly used structures in storage systems, thin-walled open sections are subjected to stability loss because of various buckling modes, including flexural, local, torsional and distortional. This research proposes a novel technique to increase the ultimate capacity of uprights, utilising bolts and spacers, under flexural and compressive loads. The proposed components are attached externally to the sections in certain pitches along the length. In this regard, axial tests were performed on 72 upright frames and nine single uprights with various lengths and thicknesses. Also, the impact of using reinforcing elements was evaluated by investigating the failure modes and ultimate load results. It was concluded that the reinforcement technique is able to restrain upright flanges and therefore improve the upright profiles' strength. For testing the flexural behaviour, 18 samples of three types were made, including non-reinforced sections and two types of sections reinforced along the upright length at different pitches. After that, monotonic loading was applied along both the minor and major axes of the samples. The suggested reinforcing method leads to increasing the flexural capacity of the upright sections about both the major and minor axes. Also, by using reinforcing system, the flexural performance was improved, and buckling and deformation were constrained. In addition, the reinforcement technique was evaluated by Finite Element (FE) method. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) algorithms were deployed to predict the normalised ultimate load and deflection of the profiles. Following the empirical tests, the axial and flexural performance of different CFS upright profiles with various lengths, thicknesses and reinforcement spacings were simulated and examined. It was shown that the reinforcing technique improved the capacity of the samples. Consequently, the proposed reinforcements could be considered a highly effective and low-cost technique to strengthen the axial and flexural behaviour of open CFS sections considering a trade-off between performance and cost of utilising the approach

Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models

This reprint contains 15 papers published in the Special Issue entitled "Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models”. Dealing with the broad spectrum of the mechanical behavior of concrete materials and structures, the reprint includes experimental findings and numerical analyses using both conventional and advanced methodologies. This book presents contributions in the field of not only ordinary and prestressed concretes, but also special concretes, including high-strength, recycled, and fiber-reinforced concretes, for both structural and non-structural applications, and for the development of related numerical/analytical predictive models

Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks

Reinforced concrete (RC) structural elements such as peripheral beams, beams supporting the canopy, ring beams of circular slabs are common members which could encounter torsional moments. One of the up-to-date and modern techniques for strengthening such beams is using FRP sheets. The increase of service loads, degradation of mechanical properties and updates in code regulations would cause to the need for retrofit. Using FRP sheets as a strengthening technique would increase the flexure, shear and torsion capacity. It would also change the failure mode and/or plane. In this article, torsional strength prediction of RC beams strengthened by FRP using artificial neural networks has been investigated. Input parameters of ANNs are RC beam width, height, FRP sheet thickness, modulus of elasticity of FRP sheet, yielding stress of longitudinal and transverse steels, the compressive strength of concrete, the effective width of shear strips along beam length, center-to-center space of FRP strips, the angle of wrapping and number of FRP layers. The target parameter is the capacity of the beam in bearing the torsional moment. The results indicate that the idealized neural network having definite number of neurons in hidden layer, can predict the torsional strength of RC beams externally bonded with FRP with a high degree of precision. Considering the mentioned precision, the method could be an efficient alternative for time-consuming and highly cost experimental programs

Optimal seismic retrofitting of existing RC frames through soft-computing approaches

2016 - 2017Ph.D. Thesis proposes a Soft-Computing approach capable of supporting the engineer judgement in the selection and design of the cheapest solution for seismic retrofitting of existing RC framed structure. Chapter 1 points out the need for strengthening the existing buildings as one of the main way of decreasing economic and life losses as direct consequences of earthquake disasters. Moreover, it proposes a wide, but not-exhaustive, list of the most frequently observed deficiencies contributing to the vulnerability of concrete buildings. Chapter 2 collects the state of practice on seismic analysis methods for the assessment the safety of the existing buildings within the framework of a performancebased design. The most common approaches for modeling the material plasticity in the frame non-linear analysis are also reviewed. Chapter 3 presents a wide state of practice on the retrofitting strategies, intended as preventive measures aimed at mitigating the effect of a future earthquake by a) decreasing the seismic hazard demands; b) improving the dynamic characteristics supplied to the existing building. The chapter presents also a list of retrofitting systems, intended as technical interventions commonly classified into local intervention (also known “member-level” techniques) and global intervention (also called “structure-level” techniques) that might be used in synergistic combination to achieve the adopted strategy. In particular, the available approaches and the common criteria, respectively for selecting an optimum retrofit strategy and an optimal system are discussed. Chapter 4 highlights the usefulness of the Soft-Computing methods as efficient tools for providing “objective” answer in reasonable time for complex situation governed by approximation and imprecision. In particular, Chapter 4 collects the applications found in the scientific literature for Fuzzy Logic, Artificial Neural Network and Evolutionary Computing in the fields of structural and earthquake engineering with a taxonomic classification of the problems in modeling, simulation and optimization. Chapter 5 “translates” the search for the cheapest retrofitting system into a constrained optimization problem. To this end, the chapter includes a formulation of a novel procedure that assembles a numerical model for seismic assessment of framed structures within a Soft-Computing-driven optimization algorithm capable to minimize the objective function defined as the total initial cost of intervention. The main components required to assemble the procedure are described in the chapter: the optimization algorithm (Genetic Algorithm); the simulation framework (OpenSees); and the software environment (Matlab). Chapter 6 describes step-by-step the flow-chart of the proposed procedure and it focuses on the main implementation aspects and working details, ranging from a clever initialization of the population of candidate solutions up to a proposal of tuning procedure for the genetic parameters. Chapter 7 discusses numerical examples, where the Soft-Computing procedure is applied to the model of multi-storey RC frames obtained through simulated design. A total of fifteen “scenarios” are studied in order to assess its “robustness” to changes in input data. Finally, Chapter 8, on the base of the outcomes observed, summarizes the capabilities of the proposed procedure, yet highlighting its “limitations” at the current state of development. Some possible modifications are discussed to enhance its efficiency and completeness. [edited by author]XVI n.s

Neural networks for the rapid seismic assessment of existing moment-frame RC buildings

The study presented in this paper analyses and investigates the possibility of introducing a general and rapid methodology based on an artificial neural network (ANN) to assess the seismic response of existing reinforced concrete (RC) buildings. Starting from investigations carried out on buildings located in the outskirts of Bologna, 928 finite element models have been developed on the basis of the most recurrent data. The input parameters representing the characteristics of the structures were systematically varied and, through modal dynamic and non-linear static analyses, the outputs representing the seismic response were recorded. The resulting dataset was used to create a function, based on ANN, that can reliably predict the seismic behaviour of a RC structure. Finally, by means of k-fold cross-validation, the instruction of the function was optimised and simultaneously verified, obtaining a coefficient of determination for the totality of the samples and the previously unseen cases of 0,94 and 0,88, respectively. The result obtained not only aims at enriching the existing framework on the subject, increasing the awareness of the seismic issues affecting this building typology, but also constitutes a prioritization system that could highlight the need for structural renovation

Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP

To provide lateral resistance in structures as well as buildings, there are some types of structural systems such as shear walls. The utilization of lateral loads occurs on a plate on the wall's vertical dimension. Conventionally, these sorts of loads are transferred to the wall collectors. There is a significant resistance between concrete shear walls and lateral seismic loading. To guarantee the building's seismic security, the shear strength of the walls has to be prognosticated by using models. This paper aims to predict shear strength by using Artificial Neural Network (ANN), Neural Network-Based Group Method of Data Handling (GMDH-NN), and Gene Expression Programming (GEP). The concrete's compressive strength, the yield strength of transverse reinforcement, the yield strength of vertical reinforcement, the axial load, the aspect ratio of the dimensions, the wall length, the thickness of the reinforced concrete shear wall, the transverse reinforcement ratio, and the vertical reinforcement ratio are the input parameters for the neural network model. And the shear strength of the reinforced concrete shear wall is considered as the target parameter of the ANN model. The results validate the capability of the models predicted by ANN, GMDH-NN, and GEP, which are suitable for use as a tool for predicting the shear strength of concrete shear walls with high accuracy

Artificial Intelligence in Civil Infrastructure Health Monitoring—historical Perspectives, Current Trends, and Future Visions

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research

Efficient design of post-tensioned concrete box-girder road bridges based on sustainable multi-objective criteria

[EN] Bridges, as an important component of infrastructure, are expected to meet all the requirements for a modern society. Traditionally, the primary aim in bridge design has been to achieve the lowest cost while guaranteeing the structural efficiency. However, concerns regarding building a more sustainable future have change the priorities of society. Ecological and durable structures are increasingly demanded. Under these premises, heuristic optimization methods provide an effective alternative to structural designs based on experience. The emergence of new materials, structural designs and sustainable criteria motivate the need to create a methodology for the automatic and accurate design of a real post-tensioned concrete bridge that considers all these aspects. For the first time, this thesis studies the efficient design of post-tensioned concrete box-girder road bridges from a sustainable point of view. This research integrates environmental, safety and durability criteria into the optimum design of the bridge. The methodology proposed provides multiple trade-off solutions that hardly increase the cost and achieve improved safety and durability. Likewise, this approach quantifies the sustainable criteria in economic terms, and evaluates the effect of these criteria on the best values of the variables. In this context, a multi-objective optimization is formulated to provide multiple trade-off and high-performing solutions that balance economic, ecologic and societal goals. An optimization design program selects the best geometry, concrete type, reinforcement and post-tensioning steel that meet the objectives selected. A three-span continuous box-girder road bridge located in a coastal region is selected for a case study. This approach provides vital knowledge about this type of bridge in the sustainable context. The life-cycle perspective has been included through a lifetime performance evaluation that models the bridge deterioration process due to chloride-induced corrosion. The economic, environmental and societal impacts of maintenance actions required to extend the service life are examined. Therefore, the proposed goals for an efficient design have been switch from initial stage to life-cycle consideration. Faced with the large computational time of multi-objective optimization and finite-element analysis, artificial neural networks (ANNs) are integrated in the proposed methodology. ANNs are trained to predict the structural response based on the design variables, without the need to analyze the bridge response. The multi-objective optimization problem results in a set of trade-off solutions characterized by the presence of conflicting objectives. The final selection of preferred solutions is simplified by a decision-making technique. A rational technique converts a verbal pairwise comparison between criteria with a degree of uncertainty into numerical values that guarantee the consistency of judgments. This thesis gives a guide for the sustainable design of concrete structures. The use of the proposed approach leads to designs with lower life-cycle cost and emissions compared to general design approaches. Both bridge safety and durability can be improved with a little cost increment by choosing the correct design variables. In addition, this methodology is applicable to any type of structure and material.[ES] Los puentes, como parte importante de una infraestructura, se espera que reúnan todos los requisitos de una sociedad moderna. Tradicionalmente, el objetivo principal en el diseño de puentes ha sido lograr el menor coste mientras se garantiza la eficiencia estructural. Sin embargo, la preocupación por construir un futuro más sostenible ha provocado un cambio en las prioridades de la sociedad. Estructuras más ecológicas y duraderas son cada vez más demandadas. Bajo estas premisas, los métodos de optimización heurística proporcionan una alternativa eficaz a los diseños estructurales basados en la experiencia. La aparición de nuevos materiales, diseños estructurales y criterios sostenibles motivan la necesidad de crear una metodología para el diseño automático y preciso de un puente real de hormigón postesado que considere todos estos aspectos. Por primera vez, esta tesis estudia el diseño eficiente de puentes de hormigón postesado con sección en cajón desde un punto de vista sostenible. Esta investigación integra criterios ambientales, de seguridad estructural y durabilidad en el diseño óptimo del puente. La metodología propuesta proporciona múltiples soluciones que apenas encarecen el coste y mejoran la seguridad y durabilidad. Al mismo tiempo, se cuantifica el enfoque sostenible en términos económicos, y se evalúa el efecto que tienen dichos criterios en el valor óptimo de las variables. En este contexto, se formula una optimización multiobjetivo que proporciona soluciones eficientes y de compromiso entre los criterios económicos, ecológicos y sociales. Un programa de optimización del diseño selecciona la mejor combinación de geometría, tipo de hormigón, armadura y postesado que cumpla con los objetivos seleccionados. Se ha escogido como caso de estudio un puente continuo en cajón de tres vanos situado en la costa. Este método proporciona un mayor conocimiento sobre esta tipología de puentes desde un punto de vista sostenible. Se ha estudiado el ciclo de vida a través de la evaluación del deterioro estructural del puente debido al ataque por cloruros. Se examina el impacto económico, ambiental y social que produce el mantenimiento necesario para extender la vida útil del puente. Por lo tanto, los objetivos propuestos para un diseño eficiente han sido trasladados desde la etapa inicial hasta la consideración del ciclo de vida. Para solucionar el problema del elevado tiempo de cálculo debido a la optimización multiobjetivo y el análisis por elementos finitos, se han integrado redes neuronales en la metodología propuesta. Las redes neuronales son entrenadas para predecir la respuesta estructural a partir de las variables de diseño, sin la necesidad de analizar el puente. El problema de optimización multiobjetivo se traduce en un conjunto de soluciones de compromiso que representan objetivos contrapuestos. La selección final de las soluciones preferidas se simplifica mediante una técnica de toma de decisiones. Una técnica estructurada convierte los juicios basados en comparaciones por pares de elementos con un grado de incertidumbre en valores numéricos que garantizan la consistencia de dichos juicios. Esta tesis proporciona una guía que extiende y mejora las recomendaciones sobre el diseño de estructuras de hormigón dentro del contexto de desarrollo sostenible. El uso de la metodología propuesta lleva a diseños con menor coste y emisiones del ciclo de vida, comparado con diseños que siguen metodologías generales. Los resultados demuestran que mediante una correcta elección del valor de las variables se puede mejorar la seguridad y durabilidad del puente con un pequeño incremento del coste. Además, esta metodología es aplicable a cualquier tipo de estructura y material.[CA] Els ponts, com a part important d'una infraestructura, s'espera que reunisquen tots els requisits d'una societat moderna. Tradicionalment, l'objectiu principal en el disseny de ponts ha sigut aconseguir el menor cost mentres es garantix l'eficiència estructural. No obstant això, la preocupació per construir un futur més sostenible ha provocat un canvi en les prioritats de la societat. Estructures més ecològiques i durables són cada vegada més demandades. Davall estes premisses, els mètodes d'optimització heurística proporcionen una alternativa eficaç als dissenys estructurals basats en l'experiència. L'aparició de nous materials, dissenys estructurals i criteris sostenibles motiven la necessitat de crear una metodologia per al disseny automàtic i precís d'un pont real de formigó posttesat que considere tots estos aspectos. Per primera vegada, esta tesi estudia el disseny eficient de ponts de formigó posttesat amb secció en calaix des d'un punt de vista sostenible. Esta investigació integra criteris ambientals, de seguretat estructural i durabilitat en el disseny òptim del pont. La metodologia proposada proporciona múltiples solucions que a penes encarixen el cost i milloren la seguretat i durabilitat. Al mateix temps, es quantifica l'enfocament sostenible en termes econòmics, i s'avalua l'efecte que tenen els dits criteris en el valor òptim de les variables. En este context, es formula una optimització multiobjetivo que proporciona solucions eficients i de compromís entre els criteris econòmics, ecològics i socials. Un programa d'optimització del disseny selecciona la millor geometria, tipus de formigó, armadura i posttesat que complisquen amb els objectius seleccionats. S'ha triat com a cas d'estudi un pont continu en calaix de tres vans situat en la costa. Este mètode proporciona un major coneixement sobre esta tipologia de ponts des d'un punt de vista sostenible. S'ha estudiat el cicle de vida a través de l'avaluació del deteriorament estructural del pont a causa de l'atac per clorurs. S'examina l'impacte econòmic, ambiental i social que produïx el manteniment necessari per a estendre la vida útil del pont. Per tant, els objectius proposats per a un disseny eficient han sigut traslladats des de l'etapa inicial fins a la consideració del cicle de vida. Per a solucionar el problema de l'elevat temps de càlcul degut a l'optimització multiobjetivo i l'anàlisi per elements finits, s'han integrat xarxes neuronals en la metodologia proposada. Les xarxes neuronals són entrenades per a predir la resposta estructural a partir de les variables de disseny, sense la necessitat d'analitzar el pont. El problema d'optimització multiobjetivo es traduïx en un conjunt de solucions de compromís que representen objectius contraposats. La selecció final de les solucions preferides se simplifica per mitjà d'una tècnica de presa de decisions. Una tècnica estructurada convertix els juís basats en comparacions per parells d'elements amb un grau d'incertesa en valors numèrics que garantixen la consistència dels dits juís. Esta tesi proporciona una guia que estén i millora les recomanacions sobre el disseny d'estructures de formigó dins del context de desenrotllament sostenible. L'ús de la metodologia proposada porta a dissenys amb menor cost i emissions del cicle de vida, comparat amb dissenys que seguixen metodologies generals. Els resultats demostren que per mitjà d'una correcta elecció del valor de les variables es pot millorar la seguretat i durabilitat del pont amb un xicotet increment del cost. A més, esta metodologia és aplicable a qualsevol tipus d'estructura i material.García Segura, T. (2016). Efficient design of post-tensioned concrete box-girder road bridges based on sustainable multi-objective criteria [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/73147TESI

A new evolutionary polynomial regression technique to assess the fundamental periods of irregular buildings

The main seismic design codes propose simplified formulations to evaluate the fundamental period of regular structures based on the total height. Indeed, the fundamental period depends on several parameters directly connected to the mass and stiffness of the structure and on its geometrical characteristics, including also irregularities. This paper proposes a set of mathematical formulations to evaluate the longitudinal and transversal fundamental period of vibration of 3D Reinforced Concrete (RC) frames, which have various vertical and plan irregularities and for different mechanical and geometrical design parameters. Several types of Reinforced Concrete Bare Moment Resisting Frame (RC-BMRF) buildings have been designed according to the different versions of the Italian codes starting from 1916 to nowadays and then used as case studies. Modal analysis is performed on the entire building dataset to assess the fundamental periods in both longitudinal and transversal directions. Then, cluster analysis is carried out to classify the buildings based on similar design characteristics and construction details. Finally, a robust Evolutionary Polynomial Regression (EPR) technique is used to find the optimal polynomial forms of the natural period. Numerical results show a better performance of the proposed formulation compared with the existing methodologies available in the literature