Application of Support Vector Machine in the analysis of the technical state of development in the LGOM mining area

Rusek, Janusz

doi:10.17531/ein.2017.1.8

Cited by 12 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All of these approaches are classified as derivative-free optimisation (DFO) methods [22], which avoid stacking the optimisation process at a local minimum, thereby increasing the chance of attaining a global minimum. The choice of the aforementioned optimisation methods was partly motivated by the authors' experiences with the use of genetic algorithms (GA) for the problem of optimum selection of hyperparameters for the support vector machine (SVM) method from a regression standpoint [23,24].…”

Section: Methodsmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rusek,

Tajduś

2020

View full text Add to dashboard Cite

The paper presents the results of analyses concerning a new approach to approximating trajectory of mining-induced horizontal displacements. Analyses aimed at finding the most effective method of fitting data to the trajectory of mining-induced horizontal displacements. Two variants were made. In the first, the direct least square fitting (DLSF) method was applied based on the minimization of the objective function defined in the form of an algebraic distance. In the second, the effectiveness of differential-free optimization methods (DFO) was verified. As part of this study, the following methods were tested: genetic algorithms (GA), differential evolution (DE) and particle swarm optimization (PSO). The data for the analysis were measurements of on the ground surface caused by the mining progressive work at face no. 698 of the German Prospel-Haniel mine. The results obtained were compared in terms of the fitting quality, the stability of the results and the time needed to carry out the calculations. Finally, it was found that the direct least square fitting (DLSF) approach is the most effective for the analyzed registration data base. In the authors' opinion, this is dictated by the angular range in which the measurements within a given measuring point oscillated.

show abstract

Section: Methodsmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rusek,

Tajduś

2020

View full text Add to dashboard Cite

show abstract

“…The risk of damage to buildings can be determined using a number of variables with a seemingly small contribution. It has been found in the course of many studies described, among others [55][56][57]. Taking this into account and the characteristics of all described methods of learning BBN from data, it was decided to use the score-based approach in the further part of the research.…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

2022

Self Cite

View full text Add to dashboard Cite

This paper presents the results of comparative studies on the implementation of machine learning methods in the damage intensity assessment of masonry buildings. The research was performed on existing residential buildings, subjected to negative impacts of the industrial environment induced by coal mining plants during their whole technical life cycle. The research was justified on the grounds of safety of use, as well as potential energy losses and CO2 emissions generated by the inefficient management of building materials resources resulting from poor planning of retrofitting. In this field, the research is in line with the global trends of large-scale retrofitting of existing buildings in European countries due to their thermal insulation parameters and seismic hazard. By combining this with the effects of material degradation throughout the technical lifecycle of buildings, the proposed methods allow for a more efficient approach to maintaining quality management of large groups of buildings, which is part of the sustainable development framework. Due to the multidimensionality of the undertaken problem and the necessity of mathematical representation of uncertainty, it was decided to implement a machine learning approach. The effectiveness of the following methods was analysed: probabilistic neural network, support vector machine, naive Bayes classification and Bayesian belief networks. The complexity of individual methods dictated the order of the adopted research horizon. Within such a research plan, both model parameters were learned, and model structure was extracted from the data, which was applied only to the approach based on Bayesian networks. The results of the conducted analyses were verified by assuming classification accuracy measures. Thus, a method was extracted that allows for the best realisation of the set research objective, which was to create a classification system to assess the intensity of damage to masonry buildings. The paper also presents in detail the characteristics of the described buildings, which were used as input variables, and assesses the effectiveness of the obtained results in terms of utilisation in practice.

show abstract

“…With this in mind and employing the experience gained from previous studies that used Machine Learning methods [28][29][30], the following was defined the subject of the study: to create a tool that can accurately predict the extent of damage to buildings, based on their degree of technical wear and the value of predicted deformation and indicators of mining tremors. To accomplish this task, classifiers from the area (family) of Machine Learning-Support Vector Machine (SVM) and Deep Learning-Convolutional Neural Network (CNN) were used.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network and Support Vector Machine for Prediction of Damage Intensity to Multi-Storey Prefabricated RC Buildings

2022

Self Cite

View full text Add to dashboard Cite

This paper presents the results of a comparative analysis of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) models created for the prediction of the extent and intensity of damage caused to multi-storey reinforced concrete (RC) buildings. The research was conducted on a group of residential buildings, which were subjected to mining impacts in the form of surface deformations and rock mass tremors during their technical life cycle. Damage to buildings poses a significant threat to the safety of the structure and the serviceability of the buildings. They are often the cause of breaks in thermal insulation, which leads to excessive consumption of thermal energy used for space heating, which in turn contributes to over-emissions of CO2 into the atmosphere. Therefore, this problem is important, not only from a technical dimension, but also includes social, economic, and environmental aspects, which allows it to be classified as an issue of sustainable development in the building industry. As a result of the conducted analysis, among the CNN models, the highest level of classification accuracy was the model obtained using the ADAM (derived from adaptive moment estimation) algorithm, which was also characterized by a very high level of generalization, obtaining 80.35% correctly classified patterns for the training set and 80.52% for the test set. However, its accuracy level was slightly lower than that of the SVM model (85.15% for the training set and 84.42% for the test set), in which Bayesian optimization was used to determine the parameters. The analysis confirmed the effectiveness of the adopted methodology for predicting the extent and intensity of damage. The developed tool can support the optimization of building maintenance management, resulting in reduced economic and environmental expenditures for renovations.

show abstract

Application of Support Vector Machine in the analysis of the technical state of development in the LGOM mining area

Cited by 12 publications

References 8 publications

Bulletin of the Polish Academy of Sciences: Technical Sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

Convolutional Neural Network and Support Vector Machine for Prediction of Damage Intensity to Multi-Storey Prefabricated RC Buildings

Contact Info

Product

Resources

About