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Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Computer Modeling in Engineering & Sciences 2024年第3期138卷 2899-2924页

作者： Ayla Ocak Umit Isıkdag gebrail bekdas Sinan Melih Nigdeli Sanghun Kim ZongWoo Geem Department of Civil Engineering Istanbul University-CerrahpasaIstanbul34320Turkey Department of Informatics Mimar Sinan Fine Arts UniversityIstanbul34427Turkey Department of Civil and Environmental Engineering Temple UniversityPhiladelphiaPA19122USA Department of Smart City Gachon UniversitySeongnam13120Korea

Base isolators used in buildings provide both a good acceleration reduction and structural vibration control *** base isolators may lose their damping capacity over time due to environmental or dynamic *** deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator *** this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over *** the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be *** this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault *** data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic *** a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class *** performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by *** the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been *** the results are examined,it has been determined that the damping of the isolator structure with the

关键词： Vibration control base isolation machine learning damping capacity

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Estimating Construction Material Indices with ARIMA and Optimized NARNETs

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Computers, Materials & Continua 2023年第1期74卷 113-129页

作者： Ümit Isıkdag Aycan Hepsag Sureyya Imre Bıyıklı DeryaÖz gebrail bekdas Zong Woo Geem Department of Informatics Mimar Sinan Fine Arts UniversityIstanbulTurkey Econometrics Department Istanbul UniversityIstanbulTurkey MIS Department Istanbul Gelisim UniversityIstanbulTurkey Computing HND Prog. Nisantasi UniversityIstanbulTurkey Department of Civil Engineering Istanbul University-CerrahpasaIstanbulTurkey College of IT Convergence Gachon UniversitySeongnam13120Korea

Construction Industry operates relying on various key economic *** of these indicators is material *** the other hand,cost is a key concern in all operations of the construction *** the uncertain conditions,reliable cost forecasts become an important source of *** cost is one of the key components of the overall cost of *** addition,cost overrun is a common problem in the construction industry,where nine out of ten construction projects face cost *** order to carry out a successful cost management strategy and prevent cost overruns,it is very important to find reliable methods for the estimation of construction material *** prices have a time dependent *** order to increase the foreseeability of the costs of construction materials,this study focuses on estimation of construction material indices through time series *** different types of analysis are implemented for estimation of the future values of construction material *** first method implemented was Autoregressive Integrated Moving Average(ARIMA),which is known to be successful in estimation of time series having a linear *** second method implementedwas Non-LinearAutoregressive Neural Network(NARNET)which is known to be successful in modeling and estimating of series with non-linear *** results have shown that depending on the nature of the series,both these methods can successfully and accurately estimate the future values of the *** addition,we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.

关键词： Construction material indices ARIMA non-linear autoregressive neural network NARNETs

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