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Machine learning-based seismic response and performance assessment of reinforced concrete buildings

Kazemi F., Asgarkhani N., Jankowski R.

Archives of Civil and Mechanical Engineering

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2023

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Vol. 23, no. 2

art. no. e94, 2023

EN

Complexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data points of training dataset for developing data-driven techniques, Incremental Dynamic Analyses (IDAs) were performed considering 165 RC MRFs with two-, to twelve-Story elevations having the bay lengths of 5.0 m, 6.1 m, and 7.6 m assuming near-fault seismic excitations. Then, important structural features were considered in datasets to train and test the ML-based prediction models, which were improved with innovative techniques. The results show that improved algorithms have higher R2 values for estimating the Maximum Interstory Drift Ratio (IDRmax), and two improved algorithms of artificial neural networks and extreme gradient boosting can estimate the Median of IDA curves (M-IDAs) of RC MRFs, which can be used to estimate the seismic limit-state capacity and performance assessment of existing or newly constructed RC buildings. To validate the generality and accuracy of the proposed ML-based prediction model, a five-Story RC building with different input features was used, and the results are promising. Therefore, graphical user interface is introduced as user-friendly tool to help researchers in estimating the seismic limit-state capacity of RC buildings, while reducing the computational cost and analytical efforts.

2

A foot joint and muscle force assessment of the running stance phase whilst wearing normal shoes and bionic shoes

Zhou Huiyu, Xu Datao, Quan Wenjing, Ugbolue Ukadike Chris, Sculthorpe Nicholas F., Baker Julien S., Gu Yaodong

Acta of Bioengineering and Biomechanics

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2022

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Vol. 24, no. 1

191--202

EN

The aim of this study was to investigate the differences in ankle joint parameters of biomechanics changes between the normal shoes (NS) and the bionic shoes (BS) during the running stance phases. Methods: A total of 40 Chinese male runners from Ningbo University were recruited for this study (age: 22.3 ± 3.01 years; height: 174.67 ± 7.11 cm; body weight (BW): 66.83 ± 9.91 kg). The participants were asked to perform a running task. Statistical parametric mapping (SPM) analysis was used to investigate any differences between NS and BS during the running stance phases. The principal component analysis (PCA) and support vector machine (SVM) were used to further explore the differences of the muscle force between the BS and NS. Results: Significant differences ( p < 0.05) were found in the first metatarsophalangeal joint (MPJ1), ground reaction force (GRF), ankle joint and around muscle forces. Furthermore, the accuracy of SVM model in identifying the gait muscle force between BS and NS reached 100%, which proved that the BS had a very large impact on the gait muscle force compared with NS. Conclusions: We found that BS may be better suited to the human condition than other unstable shoes, or even NS. In addition, our results suggest that BS play an important role in reducing ankle injuries during running by increasing muscle participation in unstable conditions while better restoring the most primitive instability of foot condition that humans have.

3

From conventional to machine learning methods for maritime riskassessment

Rawson A., Brito M., Sabeur Z., Tran-Thanh L.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2021

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Vol. 15 No. 4

757--764

EN

Within the last thirty years, the range and complexity of methodologies proposed to assess maritime risk have increased significantly. Techniques such as expert judgement, incident analysis, geometric models, domain analysis and Bayesian Networks amongst many others have become dominant within both the literature and industry. On top of this, advances in machine learning algorithms and big data have opened opportunities for new methods which might overcome some limitations of conventional approaches. Yet, determining the suitability or validity of one technique over another is challenging as it requires a systematic multicriteria approach to compare the inputs, assumptions, methodologies and results of each method. Within this paper, such an approach is proposed and tested within an isolated waterway in order to justify the proposed advantages of a machine learning approach to maritime risk assessment and should serve as inspiration for future work.

4

Wybrane algorytmy uczenia maszynowego w segmentacji obrazu kłaczków osadów ściekowych

Wodołażski Artur

Przegląd Elektrotechniczny

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2021

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R. 97, nr 8

134--136

PL

W artykule przedstawiono wybrane algorytmy uczenia maszynowego do przetwarzania obrazu mikroskopowego utlenionych kłaczków osadów ściekowych w celu oceny skuteczności monitorowania procesu tlenowej stabilizacji. Przedstawiono i porównano trzy techniki segmentacji algorytmem: k-means, fuzzy c-means oraz progowania Otsu w ocenie skuteczności segmentacji obszarów utlenionych i wykryciu zjawiska spęcznienia lub pienienia się kłaczków osadu ściekowego. Wykorzystane metryki GCE, RI, VI skutecznie porównują zmiany morfologiczne i strukturalne kłaczków poprzez ocenę segmentacji i kwantyfikacji obrazu. Analiza obrazów mikroskopowych przy wykorzystaniu technik uczenia maszynowego zapewniają oszczędność czasu i stanowią alternatywę metod fizyko-chemicznych w ocenie tlenowej stabilizacji osadu ściekowego

EN

The article presents selected machine learning algorithms for processing the microscopic image of oxidized sewage sludge flocs in order to assess the effectiveness of monitoring the oxygen stabilization process. Three techniques of segmentation were presented and compared by algorithm: k-means, fuzzy c-means and Otsu thresholding in assessing segmentation effectiveness of oxidized areas and detecting the swelling or foaming phenomenon of sewage sludge flocs. The GCE, RI, VI metrics has been effectively used and compared for morphological and structural changes of the flocs by assessing the image segmentation and quantification. The analysis of microscopic images using machine learning techniques save time and constitute an alternative to the physico-chemical methods to assessment aerobic stabilization of sewage sludge.

5

Using bayesian networks to model competence of lifeboat coxswains

Billard R., Smith J., Masharraf M., Veitch B.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2020

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Vol. 14. no. 3

585--594

EN

The assessment of lifeboat coxswain performance in operational scenarios representing offshore emergencies has been prohibitive due to risk. For this reason, human performance in plausible emergencies is difficult to predict due to the limited data that is available. The advent of lifeboat simulation provides a means to practice in weather conditions representative of an offshore emergency. In this paper, we present a methodology to create probabilistic models to study this new problem space using Bayesian Networks (BNs) to formulate a model of competence. We combine expert input and simulator data to create a BN model of the competence of slow-speed maneuvering (SSM). We demonstrate how the model is improved using data collected in an experiment designed to measure performance of coxswains in an emergency scenario. We illustrate how this model can be used to predict performance and diagnose background information about the student. The methodology demonstrates the use of simulation and probabilistic methods to increase domain awareness where limited data is available. We discuss how the methodology can be applied to improve predictions and adapt training using machine learning.

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Wykorzystanie algorytmów ewolucyjnych do wspomagania prac inżynierskich

Dobrodziej J.

Problemy Eksploatacji

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2000

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nr 4

25-34

PL

Omówiono możliwości wykorzystania algorytmów ewolucyjnych do wspomagania prac inżynierskich w wyniku pozyskiwania reguł logicznych na drodze odkrywania wiedzy w zbiorach lub bazach danych. Przedstawiono koncepcję równoległego, hierarchicznego algorytmu ewolucyjnego, przeznaczonego do wyszukiwania reguł logicznych.

EN

The paper present the possibilities of evolutionary algorithms application in engineering work support system. The algorithm was implemented as machine learning method in order to get logical rules in data files or databases. Machine learning is relatively young discipline and it is like that many new, more powerful methods will be developed in the future. The method presented here fall into the general category of inductive concept learning, which constitutes perhaps the most advanced task in machine learning.