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A Cognitive Look at Geotechnical Earthquake Engineering: Understanding the Multidimensionality of the Phenomena [chapter]

Silvia Garcia
2012 Earthquake Engineering  
It is important that readers and users of the computational models presented here familiarize themselves with the latest advances and amend the recommendations herein appropriately.  ...  Similarly, the level of ground deformation, damage to earth structures and ground failures are closely related to the severity of ground shaking. Earthquake Engineering 74 neural network.  ...  To test the predicting capabilities of the neuronal model, 186 records were excluded from the data set used in the learning phase.  ... 
doi:10.5772/50369 fatcat:goelqzcd5jeqzmz6y4aqaxeb6a

Soft Schemes for Earthquake-Geotechnical Dilemmas

Silvia García
2013 International Journal of Geophysics  
Combining the versatility of fuzzy logic to represent qualitative knowledge, the data-driven efficiency of neural networks to provide fine-tuned adjustments via local search, and the ability of genetic  ...  algorithms to perform efficient coarse-granule global search, the earthquake geotechnical problems are observed, analyzed, and solved under a holistic approach.  ...  One part of EC-genetic algorithms-are algorithms for global optimization. Genetic algorithms (GA) are based on the mechanisms of natural selection and genetics [13] .  ... 
doi:10.1155/2013/986202 fatcat:n42jtkk3arbg3izeainie3n4p4

Soft computing techniques in parameter identification and probabilistic seismic analysis of structures

Y. Tsompanakis, N.D. Lagaros, G.E. Stavroulakis
2008 Advances in Engineering Software  
The objective of this paper is to investigate the efficiency of soft computing methods, in particular methodologies based on neural networks, when incorporated into the solution of computationally intensive  ...  The back-propagation algorithm is employed for training the ANN, using data derived from selected analyses. The trained ANN is then used to predict the values of the necessary data.  ...  The changing part of the algorithm Dw (t) is further decomposed into two parts as Dw ðtÞ ¼ a t d ðtÞ ð6Þ where d (t) is a desired search direction of the move and a t the step size in that direction.  ... 
doi:10.1016/j.advengsoft.2007.06.004 fatcat:26zo6azzsrfrrkzpnysqrikd3m

Artificial Intelligence in Civil Engineering

Pengzhen Lu, Shengyong Chen, Yujun Zheng
2012 Mathematical Problems in Engineering  
Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer.  ...  Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives  ...  El-Sawalhi et al. introduced an evolved hybrid genetic algorithm and neural network GNN model 92 .  ... 
doi:10.1155/2012/145974 fatcat:asd3grpoabf5xn6tdpctxkxtwy

Adaptive remote sensing techniques implementing swarms of mobile agents

Stewart M. Cameron, Guillermo M. Loubriel, Rush D. Robinett III, Keith M. Stantz, Michael W. Trahan, John S. Wagner, Edward M. Carapezza, David B. Law, K. Terry Stalker
1999 Unattended Ground Sensor Technologies and Applications  
We have expanded intelligent control theory using physics-based collective behavior models and genetic algorithms to produce a uniquely powerfid implementation of distributed ground-based measurement incorporating  ...  These intelligent control systems integrate both globally operating decision-making systems and locally cooperative information-sharing modes using genetically-trained neural nehvorks.  ...  ACKNOWLEDGEMENTS Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy under Contract DE-AC04-94AL85000.  ... 
doi:10.1117/12.357131 fatcat:tuy57pkr5vhs5fkyasp7nfo6oe

Using Convolutional Neural Networks to Develop Starting Models for 2D Full Waveform Inversion [article]

Joseph P. Vantassel, Krishna Kumar, Brady R. Cox
2021 arXiv   pre-print
In response, we present the novel application of convolutional neural networks (CNNs) to transform an experimental seismic wavefield acquired using a linear array of surface sensors directly into a robust  ...  When compared to other common approaches, the CNN approach was able to produce starting models with smaller seismic image and waveform misfits, both before and after FWI.  ...  The construction of the seismic wavefield-image pairs and performance of traditional full waveform inversion (FWI) used the Texas Advanced Computing Center's (TACC's) cluster Stampede2 using an allocation  ... 
arXiv:2104.01626v1 fatcat:xp6mjkylazh77lzsvtitgf53hm

Estimation of renewable energy and built environment-related variables using neural networks – A review

Eugénio Rodrigues, Álvaro Gomes, Adélio Rodrigues Gaspar, Carlos Henggeler Antunes
2018 Renewable & Sustainable Energy Reviews  
Amrouche and Le Pivert [19] developed a neural network approach to forecast the global horizontal irradiance for locations with no available measured data on records.  ...  After the training phase, the models must be validated and tested against unseen data-usually a part of the original dataset. To assess their accuracy, statistical performance indicators are used.  ...  Acknowledgements The research presented has been developed under the Energy for Sustainability Initiative of the University of Coimbra (UC).  ... 
doi:10.1016/j.rser.2018.05.060 fatcat:d3ho2bapozfsbb7sae7h7g3hhy

Seismic assessment of structures and lifelines

M. Fragiadakis, D. Vamvatsikos, M.G. Karlaftis, N.D. Lagaros, M. Papadrakakis
2015 Journal of Sound and Vibration  
Our study extends from modeling seismic hazard to the modeling and analysis of structures and lifelines.  ...  A B S T R A C T We discuss the current state-of-the-art on the assessment of systems (structures and lifelines) subjected to seismic loading.  ...  Soft-computing design methods -Search Algorithms and Neural Networks Traditionally, structural analysis methods were based on rigorous scientific procedures that are formed on symmetry and uniformity.  ... 
doi:10.1016/j.jsv.2013.12.031 fatcat:pcuwnxkji5hs5b6w6qr33n5goq

Seismic Reliability-Based Design Optimization of Reinforced Concrete Structures Including Soil-Structure Interaction Effects [chapter]

Mohsen Khatibinia, Sadjad Gharehbaghi, Abbas Moustafa
2015 Earthquake Engineering - From Engineering Seismology to Optimal Seismic Design of Engineering Structures  
Acknowledgements Our special thanks go to Dr.  ...  Eysa Salajegheh (Distinguished Professor of Structural Engineering) in Department of Civil Engineering at Shahid Bahonar University of Kerman, Iran, for his cooperation in this research work.  ...  In this work, a new discrete gravitational search algorithm (DGSA) and a new meta-modeling framework were incorporated for RBDO of RC structures with Performance-Based Design (PBD) under seismic loading  ... 
doi:10.5772/59641 fatcat:3qnzu66abbdc7k4ypmmwn6lseq

A Holistic Review of Soft Computing Techniques

Philip O. Omolaye
2017 Applied and Computational Mathematics  
Due to notable technological convergence that brought about exponential growth in computer world, Soft Computing (SC) has played a vital role with automation capability features to new levels of complex  ...  In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision  ...  and approximation and dispositionality, incorporates stochasticity, capable of dealing with ambiguous, noisy data and to cap it all, using human mind as the driving model [168] .  ... 
doi:10.11648/j.acm.20170602.15 fatcat:pfmgraicunfijho23usu3pfn3a

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Adriaan L. van Natijne, Roderik C. Lindenbergh, Thom A. Bogaard
2020 Sensors  
This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications.  ...  Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field.  ...  To find the optimal parameters for the model, an optimisation method is applied, such as Particle Swarm Optimization (PSO), Grid Search (GS) or Genetic Algorithm (GA), all applied by Miao et al.  ... 
doi:10.3390/s20051425 pmid:32151069 pmcid:PMC7085549 fatcat:rqizvjduknduzojyswazmdtjxy

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

Md. Hasan Al Banna, Kazi Abu Taher, M. Shamim Kaiser, Mufti Mahmud, Md. Sazzadur Rahman, A. S. M. Sanwar Hosen, Gi Hwan Cho
2020 IEEE Access  
ACKNOWLEDGMENT The authors would like to thank Bangladesh University of Professionals for supporting this research.  ...  They have used an improved PSO algorithm as it is strong in global search at first and becomes good at local search eventually because of inertia weight (ω).  ...  DEEP MACHINE LEARNING 1) Deep Neural Network (DNN) This is a subclass of the ANN, which does not need handcrafted features to be fed into the network as it has the capability of calculating complex features  ... 
doi:10.1109/access.2020.3029859 fatcat:m53zn4ulq5c2neezhqa53qirca

Applied Imagery Pattern Recognition 2011

2011 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)  
In a world of rapidly changing technology as well as improving disaster response capabilities, how will remote sensing fit into the new response plans?  ...  Prior to this Dr. Davis helped create and served as Director of the Interagency Modeling and Atmospheric Assessment Center (IMAAC) to coordinate the fe deral respond to atmospheric hazards. Dr.  ...  Radar data acquired over several dates before, during, and after the record floods of spring 201 1 provided a valuable source of data fo r training and testing the classification algorithms; results with  ... 
doi:10.1109/aipr.2011.6176381 fatcat:7bbefbxrnnfjvdtun4zixrckjy

Big Data and Actuarial Science

Hossein Hassani, Stephan Unger, Christina Beneki
2020 Big Data and Cognitive Computing  
The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to  ...  We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries  ...  data, such as from online purchasing, job searches, web searches, and social media; geocoding data, which we are now often dependent upon for travel; genetics data, coming from gene and chromosome analysis  ... 
doi:10.3390/bdcc4040040 fatcat:g5war2v5zjcjxnnlsut3iqzh64

CSITSS Proceedings 2020

2019 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)  
were formulated which tested the partial searching capability of the model.  ...  The dataset is then split into training and testing data. The training data is fed to the model for training. Predictions are made on the test data and then an evaluation is done.  ...  provide a diverse set of features that allow the parallelized creation and training of Machine learning models and a large set of evaluation metrics that can be used to test the trained model to ascertain  ... 
doi:10.1109/csitss47250.2019.9031039 fatcat:yehi3bfgbva7xm74vp3a3i54pu
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