1,721,040 research outputs found
Resin and steel-reinforced resin used as injection materials in bolted connections
No full textInjection bolts are bolts in which the cavity produced by the clearance between the bolt and the wall of the hole is completely filled up with a two-component resin. Filling of the clearance is carried out through a small hole in the head of the bolt. After injection and complete curing, the connection is slip resistant. Recently the injection material, typically an epoxy resin, was modified at TU Delft by adding steel shots (spherical particles) to mitigate the effects of resin compliance in the shear connection of reusable composite (steel-concrete) structures. Experimental compressive material tests on unconfined/confined resin and steel-reinforced resin are evaluated in this chapter. The uniaxial model which combines damage mechanics and the Ramberg-Osgood relationship is proposed to describe the uniaxial compressive behavior of resin and steel-reinforced resin. First-order numerical homogenization is employed as a high-fidelity model, where a combined nonlinear isotropic/kinematic cyclic hardening model is employed to define the steel plasticity, the linear Drucker-Prager plastic criterion was used to simulate resin damage, and the cohesive surfaces reflecting the relationship between traction and displacement at the interface. The linear Drucker-Prager plastic model is used as a low-fidelity model.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Steel & Composite Structure
A Hybrid Approach using the Bees Algorithm and Fuzzy-AHP for Supplier Selection
No full textIn this chapter, a new hybrid approach combining the Fuzzy Analytic Hierarchy Process (AHP) and the Bees Algorithm is proposed in order to solve the supplier selection problem. The Fuzzy Analytic Hierarchy Process is used to determine the importance weight of each criterion and sub-criterion considered for the supplier selection, which are quality, cost, service level, supplier profile and risk. These weights are utilized in a mathematical model to determine the optimum order level of each row material from each supplier. The model considers capacity, demand, on-time delivery, quality and bill of materials. To determine the optimum order levels, the Bees Algorithm is applied to optimize this NP-hard type model under the constraints. The results showed that the Bees Algorithm performed better than Genetic Algorithm during the optimization stage
Lightweight cement-based materials
No full textNowadays, different properties are expected for building materials. Durability and high resistance are the common requirements, but every day more applications demand reduced weight as a required property. As a means of reducing environmental impact, sustainable construction requires considering not only the type of materials used, but also the way they affect the building process and the long-term performance of the structure. Lightweight materials are known for their insulating properties, thus low-energy conditioning of housing is one of the main reasons for their use in construction. However, these materials usually have poor mechanical properties. For this reason, lightweight materials are mostly used in nonstructural applications, such as covering mortars for walls, flat roof insulation, or concrete sandwich wall panels. In these cases, different approaches to lower the density of cement-based materials can be considered, such as using lightweight/low-strength aggregates, like expanded polystyrene (EPS), or increasing the water-to-cement ratio.Despite these approaches that tend to reduce the mechanical properties, it is also possible to reduce the density of a material and maintain a satisfactory strength. For example, cement can be replaced with lightweight/high-strength aggregates such as hollow glass microspheres (HGMSs), which are widely used for oil well lightweight cement slurries. These slurries usually require low density, as the pressure they exert over the formation when pumping it, which directly depends on density, is a limiting variable for its application. Another way to obtain lightweight cementbased materials with acceptable resistance is to enhance the mechanical properties of the binder.This chapter reviews three techniques for lightening cement-based materials: using EPS, HGMSs, and additives known in the oil well industry as extenders. Both insulating and mechanical properties are specially studied.Fil: Piqué, Teresa María. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Giurich, Matias Gaston. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Martín, Christian Marcelo. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Spinazzola, Maria Marta. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Manzanal, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long". Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long"; Argentina. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ingeniería - Sede Comodoro; Argentina. Universidad Politécnica de Madrid; Españ
Application of Particle Swarm Optimization to Solve Robotic Assembly Line Balancing Problems
No full textAssembly line balancing (ALB) problems mainly deal with proper allocation of tasks to the workstations in a balanced manner without violating the precedence relationship and optimizing a given objective function. This problem mainly occurs in a continuous production line and is classified as one of the hard optimization problems. Since the installation of assembly line is a long-term decision and highly cost intensive, there is a proper need of designing the assembly line and balancing the workload at the workstations. Over the years, human workforce has been replaced by robots for performing assembly tasks in the industries. Different types of robots with different capacity and specialization are available there is high requirement of selecting the best-fit robot to perform the tasks in the assembly line. Hence, this leads to the development of robotic assembly line balancing (RALB) problems. In this chapter, detailed implementation procedure for using metaheuristics to solve RALB problems with an objective of minimizing the cycle time is presented. Two configurations of robotic assembly line (straight and U-shaped) are discussed in detail. Particle swarm optimization (PSO) is used to solve the problem, experimental results obtained by using PSO algorithm are presented, and detailed discussion of the findings is reported.</p
Physical Modelling of Interaction Problems in Geotechnical Engineering
No full textPhysical Modelling is an established tool in geotechnical engineering for studying complex interaction problems involving soils. This chapter provides an over-arching narrative of different aspects of such physical modelling include the challenging issue of designing meaningful (useful) tests and interpretation of the results for predicting prototype consequences. There are mainly two types of scaled physical modelling: (a) Geotechnical Centrifuge Modelling under enhanced pseudo gravity; (b) Scaled modelling under 1-g i.e. (Earth’s gravity). Both the approaches are briefly described together with the advantages and disadvantages. Furthermore, this chapter also discusses the two types of methods for designing and scaling model tests: (a) Use of standard scaling laws available in textbooks which is “Black-box” type modelling; (b) Mechanics Based Scaling. Few physical modelling examples (such as buckling instability of piles in liquefied soils, behavior of buried pipelines crossing faults and landslides, response of foundations for offshore wind turbines) are considered to show the mechanics-based scaling method. It has been shown that none of the techniques are perfect and one needs the right tool for the right job. Blackbox type modelling is suitable for simple interaction problems. However, for an unknownunknown problem (typical of a multiple interaction problem), mechanics-based scaling method is appropriate. Do’s and Don’t in physical modeling are discussed
Proven energy storage system applications for power systems stability and transition issues
No full textEnergy storage system (ESS) is one of the potential solutions for some grid applications and circumstances, specifically in the transmission and distribution networks. The increasing share of nonsynchronous generation has resulted in a rising number of issues in the grid. Transmission congestion, energy balancing needs, frequency regulation, voltage limit violation, and overload of network operating resources are some of the challenges induced by renewable generation units. Electric vehicles will pose lots of problem to the grid due to their high need for large power capacity. The main purpose of this present work is to analyze and evaluate the hybridization of proven ESSs for the delivery of digital inertia in the power grid following grid disturbances. The main directions of this chapter are to (1) assess proven energy storage for grid support stability; (2) draw special attention to grid functions relevant for ESSs; (3) highlight energy storage characterization for digital inertia and hybridized scheme; (4) implement a generic transmission model of the Danish TSO Energinet.dk, with a large wind farm to investigate some specific application of proven ESSs. The SVC unit is used and applied to improve the voltage profile in the on-land connection point of the large offshore wind farm; and (5) the future implications of a hybridized scheme to resolve transition issues to operate at large system nonsynchronous penetration
Application of Relevance Vector Machine in Seismic Attenuation Prediction
No full textThe recently introduced relevance vector machine (RVM) technique is applied to predict seismic attenuation based on rock properties. The RVM provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. It evades complexity by producing models that have structure and as a result parameterization process that is appropriate to the information content of the data. Sensitivity analysis has been also performed to investigate the importance of each of the input parameters. The results show that RVM approach has the potential to be a practical tool for determination of seismic attenuation
Slope stability analysis: a support vector machine approach
No full textArtificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem. However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%
Geotechnical Site Characterization And Liquefaction Evaluation Using Intelligent Models
No full textSite characterization is an important task in Geotechnical Engineering. In situ tests based on standard penetration test (SPT), cone penetration test (CPT) and shear wave velocity survey are popular among geotechnical engineers. Site characterization using any of these properties based on finite number of in-situ test data is an imperative task in probabilistic site characterization. These methods have been used to design future soil sampling programs for the site and to specify the soil stratification. It is never possible to know the geotechnical properties at every location beneath an actual site because, in order to do so, one would need to sample and/or test the entire subsurface profile. Therefore, the main objective of site characterization models is to predict the subsurface soil properties with minimum in-situ test data. The prediction of soil property is a difficult task due to the uncertainities. Spatial variability, measurement ‘noise’, measurement and model bias, and statistical error due to limited measurements are the sources of uncertainities.
Liquefaction in soil is one of the other major problems in geotechnical earthquake engineering. It is defined as the transformation of a granular material from a solid to a liquefied state as a consequence of increased pore-water pressure and reduced effective stress. The generation of excess pore pressure under undrained loading conditions is a hallmark of all liquefaction phenomena. This phenomena was brought to the attention of engineers more so after Niigata(1964) and Alaska(1964) earthquakes. Liquefaction will cause building settlement or tipping, sand boils, ground cracks, landslides, dam instability, highway embankment failures, or other hazards. Such damages are generally of great concern to public safety and are of economic significance. Site-spefific evaluation of liquefaction susceptibility of sandy and silty soils is a first step in liquefaction hazard assessment. Many methods (intelligent models and simple methods as suggested by Seed and Idriss, 1971) have been suggested to evaluate liquefaction susceptibility based on the large data from the sites where soil has been liquefied / not liquefied.
The rapid advance in information processing systems in recent decades directed engineering research towards the development of intelligent models that can model natural phenomena automatically. In intelligent model, a process of training is used to build up a model of the particular system, from which it is hoped to deduce responses of the system for situations that have yet to be observed. Intelligent models learn the input output relationship from the data itself. The quantity and quality of the data govern the performance of intelligent model. The objective of this study is to develop intelligent models [geostatistic, artificial neural network(ANN) and support vector machine(SVM)] to estimate corrected standard penetration test (SPT) value, Nc, in the three dimensional (3D) subsurface of Bangalore. The database consists of 766 boreholes spread over a 220 sq km area, with several SPT N values (uncorrected blow counts) in each of them. There are total 3015 N values in the 3D subsurface of Bangalore. To get the corrected blow counts, Nc, various corrections such as for overburden stress, size of borehole, type of sampler, hammer energy and length of connecting rod have been applied on the raw N values. Using a large database of Nc values in the 3D subsurface of Bangalore, three geostatistical models (simple kriging, ordinary kriging and disjunctive kriging) have been developed. Simple and ordinary kriging produces linear estimator whereas, disjunctive kriging produces nonlinear estimator. The knowledge of the semivariogram of the Nc data is used in the kriging theory to estimate the values at points in the subsurface of Bangalore where field measurements are not available. The capability of disjunctive kriging to be a nonlinear estimator and an estimator of the conditional probability is explored. A cross validation (Q1 and Q2) analysis is also done for the developed simple, ordinary and disjunctive kriging model. The result indicates that the performance of the disjunctive kriging model is better than simple as well as ordinary kriging model.
This study also describes two ANN modelling techniques applied to predict Nc data at any point in the 3D subsurface of Bangalore. The first technique uses four layered feed-forward backpropagation (BP) model to approximate the function, Nc=f(x, y, z) where x, y, z are the coordinates of the 3D subsurface of Bangalore. The second technique uses generalized regression neural network (GRNN) that is trained with suitable spread(s) to approximate the function, Nc=f(x, y, z). In this BP model, the transfer function used in first and second hidden layer is tansig and logsig respectively. The logsig transfer function is used in the output layer. The maximum epoch has been set to 30000. A Levenberg-Marquardt algorithm has been used for BP model. The performance of the models obtained using both techniques is assessed in terms of prediction accuracy. BP ANN model outperforms GRNN model and all kriging models.
SVM model, which is firmly based on the theory of statistical learning theory, uses regression technique by introducing -insensitive loss function has been also adopted to predict Nc data at any point in 3D subsurface of Bangalore. The SVM implements the structural risk minimization principle (SRMP), which has been shown to be superior to the more traditional empirical risk minimization principle (ERMP) employed by many of the other modelling techniques. The present study also highlights the capability of SVM over the developed geostatistic models (simple kriging, ordinary kriging and disjunctive kriging) and ANN models.
Further in this thesis, Liquefaction susceptibility is evaluated from SPT, CPT and Vs data using BP-ANN and SVM. Intelligent models (based on ANN and SVM) are developed for prediction of liquefaction susceptibility using SPT data from the 1999 Chi-Chi earthquake, Taiwan. Two models (MODEL I and MODEL II) are developed. The SPT data from the work of Hwang and Yang (2001) has been used for this purpose. In MODEL I, cyclic stress ratio (CSR) and corrected SPT values (N1)60 have been used for prediction of liquefaction susceptibility. In MODEL II, only peak ground acceleration (PGA) and (N1)60 have been used for prediction of liquefaction susceptibility. Further, the generalization capability of the MODEL II has been examined using different case histories available globally (global SPT data) from the work of Goh (1994).
This study also examines the capabilities of ANN and SVM to predict the liquefaction susceptibility of soils from CPT data obtained from the 1999 Chi-Chi earthquake, Taiwan. For determination of liquefaction susceptibility, both ANN and SVM use the classification technique. The CPT data has been taken from the work of Ku et al.(2004). In MODEL I, cone tip resistance (qc) and CSR values have been used for prediction of liquefaction susceptibility (using both ANN and SVM). In MODEL II, only PGA and qc have been used for prediction of liquefaction susceptibility. Further, developed MODEL II has been also applied to different case histories available globally (global CPT data) from the work of Goh (1996).
Intelligent models (ANN and SVM) have been also adopted for liquefaction susceptibility prediction based on shear wave velocity (Vs). The Vs data has been collected from the work of Andrus and Stokoe (1997). The same procedures (as in SPT and CPT) have been applied for Vs also.
SVM outperforms ANN model for all three models based on SPT, CPT and Vs data. CPT method gives better result than SPT and Vs for both ANN and SVM models. For CPT and SPT, two input parameters {PGA and qc or (N1)60} are sufficient input parameters to determine the liquefaction susceptibility using SVM model.
In this study, an attempt has also been made to evaluate geotechnical site characterization by carrying out in situ tests using different in situ techniques such as CPT, SPT and multi channel analysis of surface wave (MASW) techniques. For this purpose a typical site was selected wherein a man made homogeneous embankment and as well natural ground has been met. For this typical site, in situ tests (SPT, CPT and MASW) have been carried out in different ground conditions and the obtained test results are compared. Three CPT continuous test profiles, fifty-four SPT tests and nine MASW test profiles with depth have been carried out for the selected site covering both homogeneous embankment and natural ground. Relationships have been developed between Vs, (N1)60 and qc values for this specific site. From the limited test results, it was found that there is a good correlation between qc and Vs. Liquefaction susceptibility is evaluated using the in situ test data from (N1)60, qc and Vs using ANN and SVM models. It has been shown to compare well with “Idriss and Boulanger, 2004” approach based on SPT test data.
SVM model has been also adopted to determine over consolidation ratio (OCR) based on piezocone data. Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. SVM model outperforms all the available methods for OCR prediction
Seismic liquefaction potential assessment by using Relevance Vector Machine
No full textDetermining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine (RVM) to determine the liquefaction potential of soil by using actual cone penetration test (CPT) data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network (ANN) model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction
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