New fundamental period formulae for soil-reinforced concrete structures interaction using machine learning algorithms and ANNs

Gravett, Dewald Z.; Mourlas, Christos; Taljaard, Vicky-Lee; Bakas, Nikolaos; Markou, George; Papadrakakis, Manolis

doi:10.1016/j.soildyn.2021.106656

Cited by 31 publications

(31 citation statements)

References 56 publications

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“…One of the most important dynamic characteristics is the fundamental period, as it has a significant influence on the calculation of the seismic loads. During a seismic excitation, the interaction between the superstructure (building) and the substructure (soil) can become important as it starts to affect the stress-strain distribution within the superstructure, altering the initial expected results [1][2][3]. In general, it has been found that soil-structure interaction (SSI) can increase the fundamental period and the overall damping of the system, thus it is important to consider it in order to eliminate unsafe designs and unexpected damage development [4][5] during an earthquake excitation.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is well documented that the current design codes all fail to consider the effect that SSI has on the fundamental period of a structure [3]. There are also some shortfalls with respect to the stiffness distribution of the structure, as the effect of the shear walls is not properly taken into considerations especially in the current Eurocode 8 [6], amongst others as presented in [1]. Thus, establishing design tools that would be able to predict the dynamic properties of RC buildings is of significant importance.…”

Section: Introductionmentioning

confidence: 99%

“…The methodology foresees the use of a computationally efficient and robust 3D modelling technique known as the HYMOD approach [7][8][9] in order to perform modal analysis to investigate the effect that SSI has on the fundamental period of RC structures. The overall approach foresaw the creation of a dataset comprising out of all the modal analysis results obtained from the various numerical models [1].…”

Section: Introductionmentioning

confidence: 99%

“…Thereafter this research work foresaw [1] the development of fundamental period formulae that were validated through an out-of-plane dataset and were found to have high accuracy. A number of machine learning (ML) and artificial intelligence (AI) algorithms were used to develop predictive models (including closed-form formulae), where it was found that the ability to predict the fundamental period of RC structures through a validation out-of-sample dataset was significantly high.…”

Section: Introductionmentioning

confidence: 99%

“…A number of machine learning (ML) and artificial intelligence (AI) algorithms were used to develop predictive models (including closed-form formulae), where it was found that the ability to predict the fundamental period of RC structures through a validation out-of-sample dataset was significantly high. This research work aims to further validate the proposed fundamental period formulae [1] through the use of additional fundamental period results that were developed for the needs of this manuscript.…”

Section: Introductionmentioning

confidence: 99%

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Development of a New Fundamental Period Formula by Considering Soil-Structure Interaction With the Use of Machine Learning Algorithms

Taljaard¹,

Gravett²,

Mourlas³

et al. 2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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The fundamental period of a structure is one of the key parameters utilized in the design phase to compute the seismic-resistant forces. Although the importance of seismic-resistant buildings is well understood it has been found that the current design code formulae, which are used to predict the fundamental period of reinforced concrete (RC) buildings are quite simplistic, failing to accurately predict the natural frequency, raising many concerns with regards to their reliability. The primary objective of this research project was to develop a formula that has the ability to compute the fundamental period of an RC structure, while taking into account the soil-structure interaction phenomenon. This was achieved by using a computationally efficient and robust 3D detailed modelling approach for modal analysis obtaining the numerically predicted fundamental period of 475 models, producing a dataset with numerical results. This dataset was then used to train a machine learning algorithm to formulate three fundamental period formulae using a higher-order, nonlinear regression modelling framework. The three newly proposed formulae were evaluated during the validation phase to investigate their performance using 60 new out-of-sample modal results, where, in this work, additional validation models are created and used to test the predictive abilities of the proposed fundamental period formulae. The findings of this research report suggest that the proposed fundamental period formulae exhibit exceptional predictive capabilities for the under-study RC multi-storey buildings, where they outperform all existing de-sign code fundamental period formulae currently in effect.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development of a New Fundamental Period Formula by Considering Soil-Structure Interaction With the Use of Machine Learning Algorithms

Taljaard¹,

Gravett²,

Mourlas³

et al. 2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

Self Cite

View full text Add to dashboard Cite

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging

Gajewski,

Grabski,

Cornaggia

et al. 2023

Packag Technol Sci

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Artificial intelligence is increasingly used in various branches of engineering. In this article, artificial neural networks are used to predict the crush resistance of corrugated packaging. Among the analysed packages were boxes with ventilation openings, packages with perforations and typical flap boxes, which make the proposed estimation method very universal. Typical shallow feedforward networks were used, which are perfect for regression problems, mainly when the set of input and output parameters is small, so no complicated architecture or advanced learning techniques are required. The input parameters of the neural networks are selected so as to take into account not only the material used for the production of the packaging but also the dimensions of the box and the impact of ventilation holes and perforations on the load capacity of individual walls of the packaging. In order to maximize the effectiveness of neural network training process, the group of input parameters was changed so as to eliminate those to which the sensitivity of the model was the lowest. This allowed the selection of the optimal configuration of training pairs for which the estimation error was on the acceptable level. Finally, models of neural networks were selected, for which the training and testing error did not exceed 10%. The demonstrated effectiveness allows us to conclude that the proposed set of universal input parameters is suitable for efficient training of a single neural network model capable of predicting the compressive strength of various types of corrugated packaging.

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New fundamental period formulae for soil-reinforced concrete structures interaction using machine learning algorithms and ANNs

Cited by 31 publications

References 56 publications

Development of a New Fundamental Period Formula by Considering Soil-Structure Interaction With the Use of Machine Learning Algorithms

Development of a New Fundamental Period Formula by Considering Soil-Structure Interaction With the Use of Machine Learning Algorithms

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging

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