Wykaz publikacji wybranego autora

Janusz Rusek, dr hab. inż., prof. AGH

profesor nadzwyczajny

Wydział Geodezji Górniczej i Inżynierii Środowiska
WGGiIŚ-kgib, Katedra Geodezji Inżynieryjnej i Budownictwa

  • 2023

    [dyscyplina 1] dziedzina nauk inżynieryjno-technicznych / inżynieria lądowa, geodezja i transport

  • 2020

    [dyscyplina 1] dziedzina nauk inżynieryjno-technicznych / inżynieria lądowa i transport

  • 2018

    [dyscyplina 1] dziedzina nauk inżynieryjno-technicznych / inżynieria lądowa i transport

    [dyscyplina 2] dziedzina nauk inżynieryjno-technicznych / inżynieria środowiska, górnictwo i energetyka (25%)

[poprzednia klasyfikacja] obszar nauk technicznych / dziedzina nauk technicznych / geodezja i kartografia

Identyfikatory Autora Informacje o Autorze w systemach zewnętrznych

ORCID: 0000-0003-0368-2580 orcid iD

ResearcherID: P-4718-2016

Scopus: 7004213054

PBN: 5e709208878c28a04738ee00

OPI Nauka Polska

System Informacyjny AGH (SkOs)

1
  • Analysis of influence of geometric and material properties on dynamic resistance of overpasses subjected to the impact of mining tremors
2
  • Analysis of mining-induced delayed surface subsidence
3
  • Analysis of the influence of span geometry on the dynamic resistance of existing reinforced concrete slab road viaducts subjected to mining tremors
4
  • Assessing the influence of mining impacts on buildings using SVM and MLR method
5
  • Assessing the influence of mining impacts on buildings using SVM and MLR method
6
  • Assessing the influence of mining impacts on technical wear of masonry buildings using multiple regression analysis
7
  • Assessment of damage to portal frame buildings located in a mining area
8
  • Assessment of technical condition of prefabricated large-block building structures located in mining area using the Naive Bayes Classifier
9
  • Bayesian networks and Support Vector Classifier in damage risk assessment of RC prefabricated building structures in mining areas
10
  • BNSL GOBNILP algorithm in application to damage intensity prognostic system to RC multistorey residential buildings subjected to negative impacts of the industrial environment of mines
11
  • Convolutional neural network and support vector machine for prediction of damage intensity to multi-storey prefabricated RC buildings
12
  • Determining the dynamic characteristics of footbridges using ground-based interferometric radar
13
  • Influence of the seismic intensity of the area on the assessment of dynamic resistance of bridge structures
14
  • Machine learning for determining dynamic characteristics of portal frame buildings
15
  • Machine learning methods in damage prediction of masonry development exposed to the industrial environment of mines
16
  • Maschinelles Lernen als Werkzeug der Monte-Carlo-Methode bei Erstellung eines statistischen Modells zur Beurteilung des dynam. Widerstands von Brückenobjekten
17
  • Partial least squares regression approach in the analysis of damage intensity changes to prefabricated RC buildings during the long term of mining activity
18
  • Proposal evaluation of dynamic resistance of the existing industrial portal frame buildings to the impact of mining tremors
19
  • Proposed assessment of dynamic resistance of the existing industrial portal frame building structures to the impact of mining tremors
20
  • Score-based Bayesian belief network structure learning in damage risk modelling of mining areas building development
21
  • Selected artificial intelligence methods in the risk analysis of damage to masonry buildings subject to long-term underground mining exploitation
22
  • The concept of the application of chosen artificial intelligence methods for modeling the progression of technical wear of buildings in mining areas
23
  • The impact of intensity of the seismic area on evaluation of dynamic resistance of bridges