Wyniki wyszukiwania - BazTech

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Photogrammetry-based approach for collecting and processing information about an existing building

Kontrimovičius Robertas, Juszczyk Michał, Leśniak Agnieszka, Ustinovichius Leonas, Miedziałowski Czesław

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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Vol. 71, nr 1

art. no. e144453

EN

Due to the organization of construction works, one of the most difficult situations is when a building is planned in a heritage or a densely built-up location. Fixing an existing situation manually takes a lot of time and effort and is usually not accurate. For example, it is not always possible to measure the exact spacing between buildings at different levels and to consider all outside elements of an existing building. Improper fixation of the existing situation causes mistakes and collisions in design and the use of inappropriate construction solutions. The development and progress in technologies such as BIM, laser scanning, and photogrammetry broaden the options for supporting the management of construction projects. It is important to have an effective fast collection and processing of useful information for management processes. The purpose of this paper is to analyze and present some aspects of photogrammetry to collect and process information about existing buildings. The methodology of the study is based on the comparison of two alternative approaches, namely photogrammetry and BIM modelling. Case studies present an analysis of the quantity take-offs for selected elements and parts of the buildings based on the two approaches. In this article, the specific use of photogrammetry shows that the error between the detailed BIM model and the photogrammetry model is only 1.02% and the accuracy is 98.98%. Moreover, physical capabilities do not always allow us to measure every desired element in reality. This is followed by a discussion on the usability of photogrammetry.

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Koszty ogólne zarządu - przegląd metod określania wartości roszczeń wykonawców z tytułu opóźnień

Juszczyk Michał

Materiały Budowlane

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2022

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nr 12

98--100

PL

Celem artykułu jest przegląd metod pozwalających określić roszczenia wykonawców robót budowlanych z tytułu ich opóźnień, za które odpowiedzialność ponosi inwestor. Skupiono się na szczególnym rodzaju kosztów, jakie stanowią w przedsiębiorstwach budowlanych koszty ogólne zarządu. Omówiono wybrane metody obliczeniowe i porównano je pod względem założeń, zasad oraz danych wejściowych koniecznych do obliczeń. Porównano także obliczone wartości roszczeń. Analiza założeń i wyniki obliczeń wykazały rozbieżności pomiędzy poszczególnymi metodami.

EN

The aim of the paper is to review the methods allowing to determinate contractors claims due to delays in construction works for which the client is responsible. Focuses on the specific type of costs - namely head office overheads in construction companies. Selected methods in the form of calculation formulas are discussed. Compared in terms of their assumptions and principles as well as input data necessary for calculations. Additionally the results of claims computations are also compared on the basis of calculation example. The analysis of the methods principles and the computations results revealed discrepancies between the discussed methods.

3

Development of cost estimation models based on ANN ensembles and the SVM method

Juszczyk Michał

Civil and Environmental Engineering Reports

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2020

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Vol. 30, no. 3

48--67

EN

Cost estimation, as one of the key processes in construction projects, provides the basis for a number of project-related decisions. This paper presents some results of studies on the application of artificial intelligence and machine learning in cost estimation. The research developed three original models based either on ensembles of neural networks or on support vector machines for the cost prediction of the floor structural frames of buildings. According to the criteria of general metrics (RMSE, MAPE), the three models demonstrate similar predictive performance. MAPE values computed for the training and testing of the three developed models range between 5% and 6%. The accuracy of cost predictions given by the three developed models is acceptable for the cost estimates of the floor structural frames of buildings in the early design stage of the construction project. Analysis of error distribution revealed a degree of superiority for the model based on support vector machines.

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Analysis of labour efficiency supported by the ensembles of neural networks on the example of steel reinforcement works

Juszczyk Michał

Archives of Civil Engineering

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2020

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Vol. 66, nr 1

97--111

EN

This study presents an artificial intelligence technique based on ensemble of artificial neural networks for the purposes of analysis and prediction of labour productivity. The study focuses on the development of model that combines several artificial neural networks on the basis of real-life data collected on a construction site for steel reinforcement works. The data includes conditions, characteristics, features of steel reinforcement works and related efficiencies of workers assigned to particular tasks recorded on site. The proposed ensemble based model combines five supervised learning models - five different multilayer perceptron networks, which contribution in the prediction is weighted due to the application of generalised averaging approach. Testing results show that the proposed ensemble based model achieves the satisfactory evaluation criteria for coefficient of correlation (0.989), root-mean-squared error (2.548), mean absolute percentage error (4.65%) and maximum absolute percentage error (8.98%).

PL

Wydajność pracy ma kluczowy wpływ na czas realizacji i koszty przedsięwzięć budowlanych. W publikacji przedstawiono wyniki prac badawczych nad wykorzystaniem zespołów sztucznych sieci neuronowych w analizie i predykcji wydajności pracy na przykładzie robot zbrojarskich. Analiza została przeprowadzona w oparciu dane zbierane przez wykonawcę w trakcie realizacji robót. Celem pracy badawczej była ocena przydatności danych zebranych przez wykonawcę robot oraz proponowanego narzędzia matematycznego do analizy i predykcji wydajności pracy.