Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks

Download

Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks.pdf

Date

2021-09-08

Author

Kocamaz, Korhan
Binici, Barış
Tuncay, Kağan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

249
views

213
downloads

Application of deep learning algorithms to the problems of structural engineering is an emerging research field. Inthis study, a deep learning algorithm, namely recurrent neural network (RNN), is applied to tackle a problemrelated to the assessment of reinforced concrete buildings. Inter-storey drift ratio profile of a structure is a quiteimportant parameter while conducting assessment procedures. In general, procedures require a series of timeconsuming nonlinear dynamic analysis. In this study, an extensive RNN is trained to tackle these problems andprovide a simple tool for assessment. Aim of the study is to predict the non-linear drift demand along the heightof a structure by employing RNN for a given stiffness profile along the height, strength reduction coefficient, massdensity on a floor, number of storeys. In order to train the network, a large number of nonlinear time historyanalyses are conducted for synthetically created building models. It is shown that RNN is able to accurately predictnonlinear drift demand profile of a structure along height without conducting tedious time history analyses.Therefore, the trained RNN can serve as a drift demand estimation tool, significantly shortening the assessmentprocedure.

Subject Keywords

Recurrent neural networks, Drift prediction, Structural design

URI

https://ace2020.org/en/
https://hdl.handle.net/11511/93515

Conference Name

14th INTERNATIONAL CONGRESS ON ADVANCES IN CIVIL ENGINEERING

Collections

Department of Civil Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Structured neural networks for modeling and identification of nonlinear mechanical systems Kılıç, Ergin; Dölen, Melik; Koku, Ahmet Buğra; Department of Mechanical Engineering (2012) Most engineering systems are highly nonlinear in nature and thus one could not develop efficient mathematical models for these systems. Artificial neural networks, which are used in estimation, filtering, identification and control in technical literature, are considered as universal modeling and functional approximation tools. Unfortunately, developing a well trained monolithic type neural network (with many free parameters/weights) is known to be a daunting task since the process of loading a specific pat...
Predicting the shear strength of reinforced concrete beams using artificial neural networks Mansour, MY; Dicleli, Murat; Lee, JY; Zhang, J (Elsevier BV, 2004-05-01) The application of artificial neural networks (ANNs) to predict the ultimate shear strengths of reinforced concrete (RC) beams with transverse reinforcements is investigated in this paper. An ANN model is built, trained and tested using the available test data of 176 RC beams collected from the technical literature. The data used in the ANN model are arranged in a format of nine input parameters that cover the cylinder concrete compressive strength, yield strength of the longitudinal and transverse reinforc...
Computationally enhanced techniques for practical optimum design of steel structures Kazemzadeh Azad, Saeid; Hasançebi, Oğuzhan; Department of Civil Engineering (2014) Practical optimum design of structural systems via modern metaheuristic algorithms suffers from enormously time-consuming structural analyses to locate a reasonable design. This study is an attempt to reduce the computational effort of optimization process involved in real-life applications through development of alternative techniques to the existing computationally expensive methods. Basically two main approaches are considered as (i) investigating the algorithmic structure of the existing metaheuristics ...
Neural network based online estimation of maneuvering steady states and control limits Gürsoy, Gönenç; Yavrucuk, İlkay; Department of Aerospace Engineering (2010) This thesis concerns the design and development of neural network based predictive algorithms to predict approaching aircraft limits. Therefore, approximate dynamics of flight envelope parameters such as angle of attack and load factor are constructed using neural network augmented dynamic models. Then, constructed models are used to predict steady state responses. By inverting the models and solving for critical controls at the known envelope limits, critical control inputs are calculated as well. The perf...
Geospatial Object Detection Using Deep Networks Barut, Onur; Alatan, Abdullah Aydın (2019-01-01) In the last decade, deep learning has been drawing a huge interest due to the developments in the computational hardware and novel machine learning techniques. This progress also significantly effects satellite image analysis for various objectives, such as disaster and crisis management, forest cover, road mapping, city planning and even military purposes. For all these applications, detection of geospatial objects has crucial importance and some recent object detection techniques are still unexplored to b...

Citation Formats

K. Kocamaz, B. Binici, and K. Tuncay, “Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks,” 2021, vol. 1, Accessed: 00, 2021. [Online]. Available: https://ace2020.org/en/.