Relevant bibliographies by topics / Deep parameter optimisation

Academic literature on the topic 'Deep parameter optimisation'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Deep parameter optimisation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Deep parameter optimisation"

1

Bhattacharjee, Rohan, Debjyoti Ghosh, and Abhirup Mazumder. "A REVIEW ON HYPER-PARAMETER OPTIMISATION BY DEEP LEARNING EXPERIMENTS." Journal of Mathematical Sciences & Computational Mathematics 2, no. 4 (July 5, 2021): 532–41. http://dx.doi.org/10.15864/jmscm.2407.

Full text
Abstract:
It has been found that during the runtime of a deep learning experiment, the intermediate resultant values get removed while the processes carry forward. This removal of data forces the interim experiment to roll back to a certain initial point after which the hyper-parameters or results become difficult to obtain (mostly for a vast set of experimental data). Hyper-parameters are the various constraints/measures that a learning model requires to generalise distinct data patterns and control the learning process. A proper choice and optimization of these hyper-parameters must be made so that the learning model is capable of resolving the given machine learning problem and during training, a specific performance objective for an algorithm on a dataset is optimised. This review paper aims at presenting a Parameter Optimisation for Learning (POL) model highlighting the all-round features of a deep learning experiment via an application-based programming interface (API). This provides the means of stocking, recovering and examining parameters settings and intermediate values. To ease the process of optimisation of hyper-parameters further, the model involves the application of optimisation functions, analysis and data management. Moreover, the prescribed model boasts of a higher interactive aspect and is circulating across a number of machine learning experts, aiding further utility in data management.
APA, Harvard, Vancouver, ISO, and other styles
2

Tourki, Z., and K. Sai. "Design and loading parameter optimisation in deep drawing process." International Journal of Vehicle Design 39, no. 1/2 (2005): 25. http://dx.doi.org/10.1504/ijvd.2005.007216.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Abebe, H., V. Tyree, H. Morris, and P. T. Vernier. "SPICE BSIM3 Model Parameter Extraction and Optimisation: Practical Considerations." International Journal of Electrical Engineering & Education 44, no. 3 (July 2007): 249–62. http://dx.doi.org/10.7227/ijeee.44.3.5.

Full text
Abstract:
This tutorial paper discusses the SPICE BSIM3v3.1 model parameter extraction and optimisation strategies that show consistency and very good accuracy in circuit simulation, less than 10% error, for practical IC design application in deep submicron processes. This paper describes an approach to BSIM3v3.1 model parameter extraction that mitigates or eliminates many of the unstable circuit behaviours observed during SPICE simulations with BSIM3v3. We present here a strategy applicable to 0.18 micron CMOS technology, in which the accuracy of the final extracted model parameters is evaluated by comparing simulations of inverter gain and a 31-stage ring oscillator with measured data.
APA, Harvard, Vancouver, ISO, and other styles
4

Mihail, Laurentiu A. "ROBUST ENGINEERING CASE STUDY FOR SIMULTANEOUS PARAMETER OPTIMIZATION OF A DEEP DRILLING PROCESS." International Journal "Advanced Quality" 45, no. 1 (June 19, 2017): 27. http://dx.doi.org/10.25137/ijaq.n1.v45.y2017.p27-34.

Full text
Abstract:
The paper reflects de overall results of an experiment developed for optimising a deep peck drilling process, using an extra-long flute drill. The problem stated was the dimensional, geometrical and orientation accuracy of the holes machined by the previously mentioned machining method. The target was to improve the quality of the machined parts and to reach the maximum productivity in the same time. The optimisation method used was the Taguchi Method, with a L423 fractionated factorial array. Another important issue was to optimise several quality characteristics, simultaneously. After machining the test part on a high-speed machining flexible system, the parts were measured on a coordinate measuring machine. Finally, the data was computed assisted by an advanced quality software. The simultaneous optimisation was achieved by validated method, through several iterations based on advanced process and design of experiments knowledge. Finally, the conclusions were compared with another results, from the same research program, validating it.
APA, Harvard, Vancouver, ISO, and other styles
5

Treloar, Neythen J., Nathan Braniff, Brian Ingalls, and Chris P. Barnes. "Deep reinforcement learning for optimal experimental design in biology." PLOS Computational Biology 18, no. 11 (November 21, 2022): e1010695. http://dx.doi.org/10.1371/journal.pcbi.1010695.

Full text
Abstract:
The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence—reinforcement learning—to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.
APA, Harvard, Vancouver, ISO, and other styles
6

Damavandi, Esmaeil, Amin Kolahdooz, Yousef Shokoohi, Seyyed Ali Latifi Rostami, and Sayed Mohamadbagher Tabatabaei. "Multi-objective parameter optimisation to improve machining performance on deep drilling process." International Journal of Machining and Machinability of Materials 23, no. 5/6 (2021): 500. http://dx.doi.org/10.1504/ijmmm.2021.121196.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Brodzicki, Andrzej, Michał Piekarski, and Joanna Jaworek-Korjakowska. "The Whale Optimization Algorithm Approach for Deep Neural Networks." Sensors 21, no. 23 (November 30, 2021): 8003. http://dx.doi.org/10.3390/s21238003.

Full text
Abstract:
One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many challenges including local minima, saddle points, and vanishing gradients. In this paper, we introduce the Whale Optimisation Algorithm (WOA) based on the swarm foraging behavior of humpback whales to optimise neural network hyperparameters. We wish to stress that to the best of our knowledge this is the first attempt that uses Whale Optimisation Algorithm for the optimisation task of hyperparameters. After a detailed description of the WOA algorithm we formulate and explain the application in deep learning, present the implementation, and compare the proposed algorithm with other well-known algorithms including widely used Grid and Random Search methods. Additionally, we have implemented a third dimension feature analysis to the original WOA algorithm to utilize 3D search space (3D-WOA). Simulations show that the proposed algorithm can be successfully used for hyperparameters optimization, achieving accuracy of 89.85% and 80.60% for Fashion MNIST and Reuters datasets, respectively.
APA, Harvard, Vancouver, ISO, and other styles
8

Guzzi, Francesco, George Kourousias, Alessandra Gianoncelli, Fulvio Billè, and Sergio Carrato. "A Parameter Refinement Method for Ptychography Based on Deep Learning Concepts." Condensed Matter 6, no. 4 (October 14, 2021): 36. http://dx.doi.org/10.3390/condmat6040036.

Full text
Abstract:
X-ray ptychography is an advanced computational microscopy technique, which is delivering exceptionally detailed quantitative imaging of biological and nanotechnology specimens, which can be used for high-precision X-ray measurements. However, coarse parametrisation in propagation distance, position errors and partial coherence frequently threaten the experimental viability. In this work, we formally introduce these actors, solving the whole reconstruction as an optimisation problem. A modern deep learning framework was used to autonomously correct the setup incoherences, thus improving the quality of a ptychography reconstruction. Automatic procedures are indeed crucial to reduce the time for a reliable analysis, which has a significant impact on all the fields that use this kind of microscopy. We implemented our algorithm in our software framework, SciComPty, releasing it as open-source. We tested our system on both synthetic datasets, as well as on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
APA, Harvard, Vancouver, ISO, and other styles
9

Xiong, Fansheng, Heng Yong, Hua Chen, Han Wang, and Weidong Shen. "Biot's equations-based reservoir parameter inversion using deep neural networks." Journal of Geophysics and Engineering 18, no. 6 (December 2021): 862–74. http://dx.doi.org/10.1093/jge/gxab057.

Full text
Abstract:
Abstract Reservoir parameter inversion from seismic data is an important issue in rock physics. The traditional optimisation-based inversion method requires high computational expense, and the process exhibits subjectivity due to the nonuniqueness of generated solutions. This study proposes a deep neural network (DNN)-based approach as a new means to analyse the sensitivity of seismic attributes to basic rock-physics parameters and then realise fast parameter inversion. First, synthetic data of inputs (reservoir properties) and outputs (seismic attributes) are generated using Biot's equations. Then, a forward DNN model is trained to carry out a sensitivity analysis. One can in turn investigate the influence of each rock-physics parameter on the seismic attributes calculated by Biot's equations, and the method can also be used to estimate and evaluate the accuracy of parameter inversion. Finally, DNNs are applied to parameter inversion. Different scenarios are designed to study the inversion accuracy of porosity, bulk and shear moduli of a rock matrix considering that the input quantities are different. It is found that the inversion of porosity is relatively easy and accurate, while more information is needed to make the inversion more accurate for bulk and shear moduli. From the presented results, the new approach makes it possible to realise accurate and pointwise inverse modelling with high efficiency for actual data interpretation and analysis.
APA, Harvard, Vancouver, ISO, and other styles
10

Rolek, Jaroslaw, and Grzegorz Utrata. "Optimisation of the FE Model Based on the No-Load Test Measurement for Estimating Electromagnetic Parameters of an Induction Motor Equivalent Circuit Including the Rotor Deep-Bar Effect." Energies 14, no. 22 (November 12, 2021): 7562. http://dx.doi.org/10.3390/en14227562.

Full text
Abstract:
The various measurement procedures for determination of electromagnetic parameters for the induction motor (IM) equivalent circuits including the rotor deep-bar effect were proposed in the literature. One of them is the procedure based on the load curve test (LCT). Since the execution of the LCT can pose some difficulties, especially in industrial conditions, as an alternative, the finite element method (FEM) can be employed to simulate the IM operation under the LCT. In this work we developed the optimisation technique for the finite element (FE) model. This technique is performed with the use of the stator current space-vector components which determine the IM input active and reactive power consumption during no-load operation. Relying on the LCT simulation carried out with the optimised FE model the inductance frequency characteristic can be determined and then used as the reference characteristic in the electromagnetic parameter estimation for the IM equivalent circuit including the rotor deep-bar effect. The presented research results demonstrate proper conformity between the inductance frequency characteristics obtained from the LCT performed experimentally and determined by means of the optimised FE model. Satisfactory conformity is also achieved in the case of the torque-versus-slip frequency curves acquired from the measurement and calculated by the IM space-vector model with estimated electromagnetic parameters. All of this validates the effectiveness of the proposed technique for the FE-model optimisation and the usefulness of the presented approach using the FEM in the electromagnetic parameter estimation for the IM equivalent circuit including the rotor deep-bar effect.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Deep parameter optimisation"

1

Fujdiak, Radek. "Analýza a optimalizace datové komunikace pro telemetrické systémy v energetice." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-358408.

Full text
Abstract:
Telemetry system, Optimisation, Sensoric networks, Smart Grid, Internet of Things, Sensors, Information security, Cryptography, Cryptography algorithms, Cryptosystem, Confidentiality, Integrity, Authentication, Data freshness, Non-Repudiation.
APA, Harvard, Vancouver, ISO, and other styles
2

Bokhari, Mahmoud Abdulwahab K. "Genetic Improvement of Software for Energy E ciency in Noisy and Fragmented Eco-Systems." Thesis, 2020. http://hdl.handle.net/2440/130174.

Full text
Abstract:
Software has made its way to every aspect of our daily life. Users of smart devices expect almost continuous availability and uninterrupted service. However, such devices operate on restricted energy resources. As energy eficiency of software is relatively a new concern for software practitioners, there is a lack of knowledge and tools to support the development of energy eficient software. Optimising the energy consumption of software requires measuring or estimating its energy use and then optimising it. Generalised models of energy behaviour suffer from heterogeneous and fragmented eco-systems (i.e. diverse hardware and operating systems). The nature of such optimisation environments favours in-vivo optimisation which provides the ground-truth for energy behaviour of an application on a given platform. One key challenge in in-vivo energy optimisation is noisy energy readings. This is because complete isolation of the effects of software optimisation is simply infeasible, owing to random and systematic noise from the platform. In this dissertation we explore in-vivo optimisation using Genetic Improvement of Software (GI) for energy eficiency in noisy and fragmented eco-systems. First, we document expected and unexpected technical challenges and their solutions when conducting energy optimisation experiments. This can be used as guidelines for software practitioners when conducting energy related experiments. Second, we demonstrate the technical feasibility of in-vivo energy optimisation using GI on smart devices. We implement a new approach for mitigating noisy readings based on simple code rewrite. Third, we propose a new conceptual framework to determine the minimum number of samples required to show significant differences between software variants competing in tournaments. We demonstrate that the number of samples can vary drastically between different platforms as well as from one point of time to another within a single platform. It is crucial to take into consideration these observations when optimising in the wild or across several devices in a control environment. Finally, we implement a new validation approach for energy optimisation experiments. Through experiments, we demonstrate that the current validation approaches can mislead software practitioners to draw wrong conclusions. Our approach outperforms the current validation techniques in terms of specificity and sensitivity in distinguishing differences between validation solutions.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2020
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Deep parameter optimisation"

1

Sohn, Jeongju, Seongmin Lee, and Shin Yoo. "Amortised Deep Parameter Optimisation of GPGPU Work Group Size for OpenCV." In Search Based Software Engineering, 211–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47106-8_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Bruce, Bobby R., Jonathan M. Aitken, and Justyna Petke. "Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV." In Search Based Software Engineering, 238–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47106-8_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bastian, Blossom Treesa, and C. V. Jiji. "Enhanced Aggregated Channel Features Detector for Pedestrian Detection Using Parameter Optimisation and Deep Features." In Communications in Computer and Information Science, 126–35. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0020-2_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Baniasadi, Mehri, Andreas Husch, Daniele Proverbio, Isabel Fernandes Arroteia, Frank Hertel, and Jorge Gonçalves. "Initialisation of Deep Brain Stimulation Parameters with Multi-objective Optimisation Using Imaging Data." In Informatik aktuell, 297–302. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36932-3_62.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Deep parameter optimisation"

1

Wu, Fan, Westley Weimer, Mark Harman, Yue Jia, and Jens Krinke. "Deep Parameter Optimisation." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739480.2754648.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Bokhari, Mahmoud A., Bobby R. Bruce, Brad Alexander, and Markus Wagner. "Deep parameter optimisation on Android smartphones for energy minimisation." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3082519.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Radović, Sanja, Sabolc Pap, and Maja Turk Sekulić. "Machine learning as a support tool in wastewater treatment systems – a short review." In 11th International Symposium on Graphic Engineering and Design. University of Novi Sad, Faculty of technical sciences, Department of graphic engineering and design, 2022. http://dx.doi.org/10.24867/grid-2022-p89.

Full text
Abstract:
Machine learning (ML) is a subset of artificial intelligence (AI). It is based on teaching computers how to learn from data and how to improve with experience. This valuable technique has been increasingly supporting different spheres of life. This includes ML application in enhancement and optimisation of many ecological and environmental engineering solutions, such as wastewater treatment systems (WWTS). Complexity of processes triggers challenges in ensuring good effluent quality by adequate response to dynamic process conditions. That is why techniques such as ML which, after being trained, have strong prediction ability, have been applied in WWTS. ML facilitates understanding of correlation between input features and output targets through a data-driven approach. Different ML models have been used for this purpose. Some of the commonly used were artificial neural network (ANN) or deep neural network (DNN) model, support vector machine (SVM) and its variation support vector regression (SVR) model, random forest (RF) model and many others. More often authors apply a few different models in order to obtain the one that most appropriately works for specific problem. In wastewater management those problems are various, and could include modelling of WWT processes, prediction of certain technology performance, optimisation of technology working parameters, optimisation of the production of the materials there are being used in WWT technology etc. For instance, there are several articles which describes ML power in optimisation of material synthesis (e.g., biochar production). Application of ML led to reduction in number of runs which were necessary for obtaining the best results by applied production procedure, which saved time and was also cost-beneficial. Indeed, ML incorporation in solving or avoiding potential problems within WWTS is a promising approach which has gained more attention in recent years due to the exponential technology development and progress in artificial intelligence application.
APA, Harvard, Vancouver, ISO, and other styles
4

Liu, M., and C. Cross. "Subsea Pipeline UHB OOS Design: Structural Reliability Analysis." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61186.

Full text
Abstract:
Upheaval buckling (UHB) is a major design concern for a trenched and buried subsea pipeline operating at high temperature and pressure. A predictive assessment is necessary during the detailed engineering design and optimisation to evaluate and define any measure that may be utilised for UHB mitigation such as deep trenching, backfilling, blanket or spot rockdumping. A pre-emptive UHB structural reliability analysis (SRA) has to be performed prior to pipeline installation based on the typical trench imperfection out of straightness (OOS) statistics. The SRA results are updated once survey data is made available. A rockdump schedule can be established by incorporating appropriate safety or load factors to address uncertainties in the design parameters and as-built OOS survey measurement accuracy. This paper examines the basis for processing the OOS features from survey data and stochastic distributions assumed for SRA with a view to improving the SRA OOS analysis. A number of OOS issues are considered. To cut conservatism an alternative distribution and interpretation is proposed for the key SRA input parameters with regards to imperfections and survey resolution. The random imperfection height assumption used in the current SRA practice for UHB is thus challenged — the rationale and argument for an alternative approach are constructed through a review of stochastic process theory, additional integrity criteria, a parametric analysis and evaluation of multiple OOS survey data sets. To add to the strength of the argument, a range of engineering issues are discussed in the context of stochastic distributions of imperfections. A worked example and case study is presented leading to a rationally reduced load factor and rockdump volume requirement for OOS UHB mitigation and protection.
APA, Harvard, Vancouver, ISO, and other styles
5

Protopapadakis, Giorgois, Asteris Apostolidis, and Anestis I. Kalfas. "Explainable and Interpretable AI-Assisted Remaining Useful Life Estimation for Aeroengines." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-80777.

Full text
Abstract:
Abstract Remaining Useful Life (RUL) estimation is directly related with the application of predictive maintenance. When RUL estimation is performed via data-driven methods and Artificial Intelligence algorithms, explainability and interpretability of the model are necessary for trusted predictions. This is especially important when predictive maintenance is applied to gas turbines or aeroengines, as they have high operational and maintenance costs, while their safety standards are strict and highly regulated. The objective of this work is to study the explainability of a Deep Neural Network (DNN) RUL prediction model. An open-source database is used, which is composed by computed measurements through a thermodynamic model for a given turbofan engine, considering non-linear degradation and data points for every second of a full flight cycle. First, the necessary data pre-processing is performed, and a DNN is used for the regression model. The selection of its hyper-parameters is done using random search and Bayesian optimisation. Tests considering the feature selection and the requirements of additional virtual sensors are discussed. The generalisability of the model is performed, showing that the type of faults as well as the dominant degradation has an important effect on the overall accuracy of the model. The explainability and interpretability aspects are studied, following the Local Interpretable Model-agnostic Explanations (LIME) method. The outcomes are showing that for simple data sets, the model can better understand physics, and LIME can give a good explanation. However, as the complexity of the data increases, both the accuracy of the model drops but also LIME seems to have difficulties in giving satisfactory explanations.
APA, Harvard, Vancouver, ISO, and other styles
6

Hourcade, E. "Physics of Plutonium and Americium Recycling in PWR Using Advanced Fuel Concepts." In 12th International Conference on Nuclear Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/icone12-49604.

Full text
Abstract:
PWR waste inventory management is considered in many countries including Frances as one of the main current issues. On this subject, the French 1991 Bataille’s law set up a 15 years research program on three main axes: sub-surface storage, deep geological storage, transmutation using critical or subcritical burners. Amongst the output Actinides, Pu and Am are the 2 main contents both in term of volume and long term radio-toxicity. Waiting for the Generation IV systems implementation (2035–2050), one of the mid-term solutions for their transmutation involves the use of advanced fuels in Pressurized Water Reactors (PWR). These have to require as little modification as possible of the core internals, the cooling system and fuel cycle facilities (fabrication and reprocessing). The present paper is focussed on the reactor physics characteristics, as a preliminary step in the evaluation of options, knowing that others homogeneous and heterogeneous assemblies have been studied by the CEA ([1] to [5]). The main neutronic parameters to be considered for Pu and Am recycling in PWR are void coefficient (αvoid), Doppler coefficient (αDopp), fraction of delayed neutrons (β) and power distribution (especially for heterogeneous configurations). The modification of the moderation ratio, the opportunity to use inert matrices (targets), the optimisation of Uranium, Plutonium and Americium contents are the key parameters to play with. One of the solutions presented here is a heterogeneous assembly with regular moderation ratio composed with both target fuel rods (Pu and Am embedded in an inert matrix) and standard UO2 fuel rods. An EPR (European Pressurised Reactor) type reactor, loaded only with assemblies containing 84 peripheral targets, can reach an Americium consumption rate of [4.4; 23 kg/TWhe] depending on the assembly concept. For Pu and Am inventories stabilisation, the theoretical fraction of reactors loaded with Pu + Am or Pu assemblies is about 60%. For Americium inventory stabilisation, the fraction decreases down to 16%, but Pu is produced at a rate of 18.5 Kg/Twhe (−25% compared to one through UOX cycle).
APA, Harvard, Vancouver, ISO, and other styles
7

Kay, S., and E. Palix. "Caisson Capacity in Clay: VHM Resistance Envelope: Part 3—Extension to Shallow Foundations." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49077.

Full text
Abstract:
Suction embedded caissons are efficient and economic solutions to anchor floating structures. A more recent caisson application is to support seafloor structures such as manifolds, PLEMs, pumps, etc. For a deepwater hydrocarbon field, many types of seafloor structures are required, each with their own characteristics and slightly different design. Caisson designs increasingly use resistance envelope methodology. This eliminates non-linear 3D FE analyses (except for assessing responses or soil reactions), and facilitates probabilistic and optimisation analyses. In general, there is a requirement for a reliable method of assessing caisson capacity under general VHM load. Resistance envelope equations for “deep” circular caissons (1.5 < L/D < 6) have been presented by Kay and Palix (2010) for a wide range of soil undrained shear strength profiles. This paper extends the study to cover near-surface caissons (i.e. 0 ≤ L/D ≤ 1.5). As in previous studies, a quasi 3D non-linear finite element program (HARMONY) was the primary numerical analysis tool. Three soil shear strength profiles were investigated for 13 caisson embedment ratios. In the range 0 ≤ L/D ≤ 1.5, VHM envelope shapes transform from a “scallop” at L/D ≈ 0 into a “tongue” above a critical caisson embedment ratio (L/D)crit of about 0.5 The equations originally developed for the rotated ellipse/ellipsoid (i.e. “tongue”-shaped envelope) in Kay and Palix (2010) for L/D ≤ 1.5 have been extended for (L/D)crit ≥ L/D. All parameters are simple functions of L/D and soil shear strength profile. Major limitations and assumptions made were (a) foundation-soil tension was permitted and (b) no internal scoop failure (i.e. within the soil plug inside the caisson) was possible. These are important for low L/D: both may adversely affect VHM resistance.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Deep parameter optimisation' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography