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Dr. Michał Juszczyk
Division of Management in Civil Engineering, Faculty of Civil Engineering, Cracow University of Technology, Cracow, Poland

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Research Keywords & Expertise

0 Building information modelling (BIM)
0 Construction cost analyzes
0 Applications of neural networks and machine learning in cost modelling
0 Construction project digitization
0 Technology of construction works

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Building information modelling (BIM)

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Conference paper
Published: 31 January 2020 in Advances in Intelligent Systems and Computing
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Cost estimates are essential for construction projects success in terms of completion of a project on budget. The estimates that are delivered in the early phase of construction projects are of special importance. The paper presents results of research on applicability of artificial neural networks for early cost estimates of bridge structures. Number of multilayer perceptron networks were investigated as a core of regression models developed to support cost prediction. Basic parameters of bridge structures were used as input values, whereas real life construction costs played the role of expected output values. Data used in the course of the research consisted of information collected for 161 bridge construction projects completed in Poland. One neural network of best performance was selected to be the core of the model with the use of two-step procedure. This network’s structure was 21-2-1 activation functions applied were hyperbolic tangent for hidden layer and linear for output layer. Performance of the model in the light of applied measures such as root mean squared error, mean absolute percentage error and assessment of absolute percentage errors distribution and expectations for early cost estimates is acceptable.

ACS Style

Michał Juszczyk. Early Cost Estimates of Bridge Structures Aided by Artificial Neural Networks. Advances in Intelligent Systems and Computing 2020, 10 -20.

AMA Style

Michał Juszczyk. Early Cost Estimates of Bridge Structures Aided by Artificial Neural Networks. Advances in Intelligent Systems and Computing. 2020; ():10-20.

Chicago/Turabian Style

Michał Juszczyk. 2020. "Early Cost Estimates of Bridge Structures Aided by Artificial Neural Networks." Advances in Intelligent Systems and Computing , no. : 10-20.

Journal article
Published: 19 December 2019 in Buildings
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The completion of a bridge construction project within budget is one of the project’s key factors of success. This prerequisite is more likely to be achieved if the cost estimates, especially those provided in the early stage of a project, are realistic and close to the actual costs. The paper presents the research results on the development of a cost prediction model based on machine learning, namely the support vector machines (SVM) method, for which the input represents basic information and parameters of bridges, available in the early stage of projects. Several SVM-based regression models were investigated with the use of data collected for a number of bridge construction projects completed in Poland. Having finished the machine learning and testing processes, five of the models, of satisfying knowledge generalization ability and comparable performance, were preselected. The final selection of the best model was based on the comparison and analysis ability to predict bridge construction costs with accuracy appropriate for the early stage of projects. The general testing metrics of the finally selected model, named BCCPMSVR2, were as follows: root mean square error: 1.111; correlation coefficient of real-life bridge construction costs and costs predicted by the model: 0.980; and mean absolute percentage error: 10.94%. The research resulted in the development and introduction of an original model capable of providing early estimates of bridge construction costs with satisfactory accuracy.

ACS Style

Michał Juszczyk. On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach. Buildings 2019, 10, 2 .

AMA Style

Michał Juszczyk. On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach. Buildings. 2019; 10 (1):2.

Chicago/Turabian Style

Michał Juszczyk. 2019. "On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach." Buildings 10, no. 1: 2.

Journal article
Published: 26 November 2019 in Buildings
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The aim of this study is to build a mathematical model of the productivity of construction workers. It does so by selecting 17 factors that influence the productivity of construction workers and categorising them into five groups. For the mathematical description of the factors, fuzzy logic was used. A formula for calculating the work productivity of construction workers is proposed. The novelty of the approach proposed by the authors is rooted in the consideration of various factors that have the potential to influence the productivity of construction workers. To present the way the formula operates, a single assessment of ceiling formwork was undertaken. The verification of a model confirmed its capability of analyzing, evaluating, and predicting the productivity of construction workers with satisfying accuracy.

ACS Style

Jarosław Malara; Edyta Plebankiewicz; Michał Juszczyk. Formula for Determining the Construction Workers Productivity Including Environmental Factors. Buildings 2019, 9, 240 .

AMA Style

Jarosław Malara, Edyta Plebankiewicz, Michał Juszczyk. Formula for Determining the Construction Workers Productivity Including Environmental Factors. Buildings. 2019; 9 (12):240.

Chicago/Turabian Style

Jarosław Malara; Edyta Plebankiewicz; Michał Juszczyk. 2019. "Formula for Determining the Construction Workers Productivity Including Environmental Factors." Buildings 9, no. 12: 240.

Journal article
Published: 02 July 2019 in JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
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The paper presents an original approach to construction cost analysis and development of predictive models based on ensembles of artificial neural networks. The research was focused on the application of two alternative approaches of ensemble averaging that allow for combining a number of multilayer perceptron neural networks and developing effective models for cost predictions. The models have been developed for the purpose of forecasting construction costs of sports fields as a specific type of construction objects. The research included simulation and selection of numerous neural networks that became the members of the ensembles. The ensembles included either the networks of different types in terms of their structure and activation functions or the networks of the same type. The research also included practical implementation of the developed models for cost analysis based on a sports field BIM model. This case study examined and confirmed all of the four models’ predictive capabilities and superiority over models based on single networks for the particular problem. Verification including testing and the case study enabled selection of the best ensemble-based model that combined ten networks of different types. The proposed approach is prospective for fast cost analyses and conceptual estimates in construction projects.

ACS Style

Michał Juszczyk; Krzysztof Zima; Wojciech Lelek. FORECASTING OF SPORTS FIELDS CONSTRUCTION COSTS AIDED BY ENSEMBLES OF NEURAL NETWORKS. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2019, 25, 715 -729.

AMA Style

Michał Juszczyk, Krzysztof Zima, Wojciech Lelek. FORECASTING OF SPORTS FIELDS CONSTRUCTION COSTS AIDED BY ENSEMBLES OF NEURAL NETWORKS. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT. 2019; 25 (7):715-729.

Chicago/Turabian Style

Michał Juszczyk; Krzysztof Zima; Wojciech Lelek. 2019. "FORECASTING OF SPORTS FIELDS CONSTRUCTION COSTS AIDED BY ENSEMBLES OF NEURAL NETWORKS." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 25, no. 7: 715-729.

Journal article
Published: 19 April 2019 in Sustainability
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Green buildings may become the solution to the problem of high energy consumption, including those that are subject to verification by the relevant institutions issuing green certificates. The aim of the paper is an analysis and a brief discussion of trends related to the certification of office buildings in Poland. At the end of 2017, almost 9.7 million m2 of modern office space was available in Poland, of which 62% was a certified area. This constitutes a five percent increase in the share of certified office space in relation to the total modern office space available during 2017. In order to compare the costs and benefits of certified buildings, the costs of an office building and a certified building were simulated. The comparison was made using the idea of costs in the life cycle and calculating the Life Cycle Cost. The difference between the base building and the green one was mainly based on obtaining higher Net Present Value with lower investment expenditures for the green building. There was also a clear difference between the beginning of investment profitability for the different levels of rent.

ACS Style

Edyta Plebankiewicz; Michał Juszczyk; Renata Kozik. Trends, Costs, and Benefits of Green Certification of Office Buildings: A Polish Perspective. Sustainability 2019, 11, 2359 .

AMA Style

Edyta Plebankiewicz, Michał Juszczyk, Renata Kozik. Trends, Costs, and Benefits of Green Certification of Office Buildings: A Polish Perspective. Sustainability. 2019; 11 (8):2359.

Chicago/Turabian Style

Edyta Plebankiewicz; Michał Juszczyk; Renata Kozik. 2019. "Trends, Costs, and Benefits of Green Certification of Office Buildings: A Polish Perspective." Sustainability 11, no. 8: 2359.

Journal article
Published: 20 March 2019 in Symmetry
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Construction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of reliable estimation was the reason for developing a new model for cost estimation in construction. This paper reports some results from the authors’ broad research on the modelling processes in engineering related to estimation of construction costs using artificial intelligence tools. The aim of this work was to develop a model capable of predicting a construction site cost index that would benefit from combining several artificial neural networks into an ensemble. Combining selected neural networks and forming the ensemble-based models compromised their strengths and weaknesses. With the use of data including training patterns collected on the basis of studies of completed construction projects, the authors investigated various types of neural networks in order to select the members of the ensemble. Finally, three models that were assessed in terms of performance and prediction quality were proposed. The results revealed that the developed models based on ensemble averaging and stacked generalisation met the expectations of knowledge generalisation and accuracy of prediction of site overhead cost index. The proposed models offer predictions of cost in an accepted error range and prove to deliver better predictions than those based on single neural networks. The developed tools can be used in the decision-making process regarding construction cost estimation.

ACS Style

Michał Juszczyk; Agnieszka Leśniak. Modelling Construction Site Cost Index Based on Neural Network Ensembles. Symmetry 2019, 11, 411 .

AMA Style

Michał Juszczyk, Agnieszka Leśniak. Modelling Construction Site Cost Index Based on Neural Network Ensembles. Symmetry. 2019; 11 (3):411.

Chicago/Turabian Style

Michał Juszczyk; Agnieszka Leśniak. 2019. "Modelling Construction Site Cost Index Based on Neural Network Ensembles." Symmetry 11, no. 3: 411.

Conference paper
Published: 21 January 2019 in IOP Conference Series: Earth and Environmental Science
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The paper presents some results of the research into a development of a cost estimating model that is capable of using information from building information model and implementing machine learning for cost prediction. Accurate estimates, provided throughout the whole construction project, allow for actual cost savings and assist in achieving sustainability goals. The model which is based on the support vector regression and radial basis kernel functions has been developed and proposed to support cost estimates of building's floor structural frames. The author's main assumption was to combine the benefits of building information modelling - namely the ability to extract certain information about the building and structural members of the floor frames from the models and the capabilities of machine learning. The research, presented in this paper, came down to solving a regression problem with the use of the support vectors approach. The training data for machine learning included inputs that represented features of the building and structural members' belonging to the floor structural frames and outputs that represented corresponding real life cost estimates of the floor structural frames. The obtained results show that the proposed model allows predicting costs with satisfactory accuracy.

ACS Style

M Juszczyk. Cost Estimates of Buildings’ Floor Structural Frames with the Use of Support Vector Regression. IOP Conference Series: Earth and Environmental Science 2019, 222, 012007 .

AMA Style

M Juszczyk. Cost Estimates of Buildings’ Floor Structural Frames with the Use of Support Vector Regression. IOP Conference Series: Earth and Environmental Science. 2019; 222 (1):012007.

Chicago/Turabian Style

M Juszczyk. 2019. "Cost Estimates of Buildings’ Floor Structural Frames with the Use of Support Vector Regression." IOP Conference Series: Earth and Environmental Science 222, no. 1: 012007.

Conference paper
Published: 03 September 2018 in MATEC Web of Conferences
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Cost analyses, and the conceptual cost estimates among them, are of the key importance for the construction projects successes. Implementation of neural networks or machine learning methods provides broad possibilities for this specific type of cost. The aim of the paper is to present some results of the studies on the use of support vector regression as a machine learning tool for conceptual cost estimates of residential buildings. Results for three models based on support vector regression and radial basis kernel functions are introduced.

ACS Style

Michał Juszczyk. Residential buildings conceptual cost estimates with the use of support vector regression. MATEC Web of Conferences 2018, 196, 04090 .

AMA Style

Michał Juszczyk. Residential buildings conceptual cost estimates with the use of support vector regression. MATEC Web of Conferences. 2018; 196 ():04090.

Chicago/Turabian Style

Michał Juszczyk. 2018. "Residential buildings conceptual cost estimates with the use of support vector regression." MATEC Web of Conferences 196, no. : 04090.

Conference paper
Published: 10 July 2018 in INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017)
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Sports fields can be considered as a type of sport facilities that must be built in the course of a construction project. The paper presents some results of a broader research on the problem of cost predicting for such objects supported by various mathematical tools. The Kohonen’s neural networks (also known as self-organizing map or self-organizing feature maps) are explored for the purpose of clustering data including characteristic parameters of sports fields built in Poland. Kohonen’s neural networks were applied to perform the transformation of n-dimensional input data space into a two dimensional in a map. In the course of the research, the data including characteristic parameters of sports fields was presented to the number of networks that varied in the configuration of an output layer. The two dimensional topologically ordered feature map of data clusters that describe groups of similar sports fields is proposed as a result of the analysis.

ACS Style

Michał Juszczyk; Krzysztof Zima. Clustering of sports fields as specific construction objects aided by Kohonen’s neural networks. INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017) 2018, 1978, 240009 .

AMA Style

Michał Juszczyk, Krzysztof Zima. Clustering of sports fields as specific construction objects aided by Kohonen’s neural networks. INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). 2018; 1978 (1):240009.

Chicago/Turabian Style

Michał Juszczyk; Krzysztof Zima. 2018. "Clustering of sports fields as specific construction objects aided by Kohonen’s neural networks." INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017) 1978, no. 1: 240009.

Journal article
Published: 01 July 2018 in Archives of Civil and Mechanical Engineering
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Overheads, especially site overhead costs, constitute a significant component of a contractor's budget in a construction project. The estimation of site overhead costs based on traditional approach is either accurate but time consuming (in case of the use of detailed analytical methods) or fast but inaccurate (in case of the use of index methods). The aim of the research presented in this paper was to develop an alternative model which allows fast and reliable estimation of site overhead costs. The paper presents the results of the authors’ work on development of a regression model, based on artificial neural networks, that enables prediction of the site overhead cost index, which used in conjunction with other cost data, allows to estimate site overhead costs. To develop the model, a database including 143 cases of completed construction projects was used. The modelling involved a number of artificial neural networks of the multilayer perceptrons type, each with varying structures, activation functions and training algorithms. The neural network selected to be the core of developed model allows the prediction of the costs’ index and aids in the estimation of the site overhead costs in the early stages of a construction project with satisfactory precision.

ACS Style

Agnieszka Leśniak; Michał Juszczyk. Prediction of site overhead costs with the use of artificial neural network based model. Archives of Civil and Mechanical Engineering 2018, 18, 973 -982.

AMA Style

Agnieszka Leśniak, Michał Juszczyk. Prediction of site overhead costs with the use of artificial neural network based model. Archives of Civil and Mechanical Engineering. 2018; 18 (3):973-982.

Chicago/Turabian Style

Agnieszka Leśniak; Michał Juszczyk. 2018. "Prediction of site overhead costs with the use of artificial neural network based model." Archives of Civil and Mechanical Engineering 18, no. 3: 973-982.

Research article
Published: 14 March 2018 in Complexity
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Cost estimates are essential for the success of construction projects. Neural networks, as the tools of artificial intelligence, offer a significant potential in this field. Applying neural networks, however, requires respective studies due to the specifics of different kinds of facilities. This paper presents the proposal of an approach to the estimation of construction costs of sports fields which is based on neural networks. The general applicability of artificial neural networks in the formulated problem with cost estimation is investigated. An applicability of multilayer perceptron networks is confirmed by the results of the initial training of a set of various artificial neural networks. Moreover, one network was tailored for mapping a relationship between the total cost of construction works and the selected cost predictors which are characteristic of sports fields. Its prediction quality and accuracy were assessed positively. The research results legitimatize the proposed approach.

ACS Style

Michał Juszczyk; Agnieszka Leśniak; Krzysztof Zima. ANN Based Approach for Estimation of Construction Costs of Sports Fields. Complexity 2018, 2018, 1 -11.

AMA Style

Michał Juszczyk, Agnieszka Leśniak, Krzysztof Zima. ANN Based Approach for Estimation of Construction Costs of Sports Fields. Complexity. 2018; 2018 ():1-11.

Chicago/Turabian Style

Michał Juszczyk; Agnieszka Leśniak; Krzysztof Zima. 2018. "ANN Based Approach for Estimation of Construction Costs of Sports Fields." Complexity 2018, no. : 1-11.

Conference paper
Published: 01 January 2018 in INTERNATIONAL SYMPOSIUM ON MATERIAL SCIENCE AND ENGINEERING 2018: ISMSE 2018
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This paper reports some results of the studies on the use of artificial intelligence tools for the purposes of cost estimation based on building information models. A problem of the cost estimates based on the building information models on a macro level supported by the ensembles of artificial neural networks is concisely discussed. In the course of the research a regression model has been built for the purposes of cost estimation of buildings’ floor structural frames, as higher level elements. Building information models are supposed to serve as a repository of data used for the purposes of cost estimation. The core of the model is the ensemble of neural networks. The developed model allows the prediction of cost estimates with satisfactory accuracy.

ACS Style

Michał Juszczyk. Implementation of the ANNs ensembles in macro-BIM cost estimates of buildings’ floor structural frames. INTERNATIONAL SYMPOSIUM ON MATERIAL SCIENCE AND ENGINEERING 2018: ISMSE 2018 2018, 1946, 020014 .

AMA Style

Michał Juszczyk. Implementation of the ANNs ensembles in macro-BIM cost estimates of buildings’ floor structural frames. INTERNATIONAL SYMPOSIUM ON MATERIAL SCIENCE AND ENGINEERING 2018: ISMSE 2018. 2018; 1946 (1):020014.

Chicago/Turabian Style

Michał Juszczyk. 2018. "Implementation of the ANNs ensembles in macro-BIM cost estimates of buildings’ floor structural frames." INTERNATIONAL SYMPOSIUM ON MATERIAL SCIENCE AND ENGINEERING 2018: ISMSE 2018 1946, no. 1: 020014.

Journal article
Published: 23 May 2017 in Przegląd Naukowy Inżynieria i Kształtowanie Środowiska
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Artykuł przedstawia podejście, w którym połączone zostały koncepcja analiz kosztowych macro BIM oraz zastosowania narzędzi sztucznej inteligencji – sztucznych sieci neuronowych. W artykule zaprezentowano dyskusję i podstawowe założenia proponowanego podejścia, stanowiące wyjaśnienie istoty problemu. Studium przypadku przedstawia wyniki wstępnych badań dotyczących zastosowań sieci neuronowych w analizach kosztów z zastosowaniem BIM na przykładzie oszacowań kosztów konstrukcji nośnej kondygnacji budynku. Uzyskane wyniki uzasadniają propozycję wykorzystania sieci neuronowych jako narzędzia matematycznego w przedstawionym w artykule problemie.

ACS Style

Michał Juszczyk. Studies on the ANN implementation in the macro BIM cost analyzes. Przegląd Naukowy Inżynieria i Kształtowanie Środowiska 2017, 26, 183 -192.

AMA Style

Michał Juszczyk. Studies on the ANN implementation in the macro BIM cost analyzes. Przegląd Naukowy Inżynieria i Kształtowanie Środowiska. 2017; 26 (2):183-192.

Chicago/Turabian Style

Michał Juszczyk. 2017. "Studies on the ANN implementation in the macro BIM cost analyzes." Przegląd Naukowy Inżynieria i Kształtowanie Środowiska 26, no. 2: 183-192.

Journal article
Published: 01 January 2017 in Procedia Engineering
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ACS Style

Michał Juszczyk. The Challenges of Nonparametric Cost Estimation of Construction Works with the use of Artificial Intelligence Tools. Procedia Engineering 2017, 196, 415 -422.

AMA Style

Michał Juszczyk. The Challenges of Nonparametric Cost Estimation of Construction Works with the use of Artificial Intelligence Tools. Procedia Engineering. 2017; 196 ():415-422.

Chicago/Turabian Style

Michał Juszczyk. 2017. "The Challenges of Nonparametric Cost Estimation of Construction Works with the use of Artificial Intelligence Tools." Procedia Engineering 196, no. : 415-422.

Conference paper
Published: 01 January 2017 in INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016)
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The self-organizing maps (SOM) are useful tools for the purposes of the data exploration. Their ability to transform n-dimensional signal pattern into two dimensional map is used in this paper to cluster provinces of Poland. Main assumption was to perform the clustering on the basis of statistical information concerning characteristics of construction industry. Output of construction industry and number of completed construction objects ordered by provinces was presented to the number of SOM neural networks. As a result of the computations and neural simulations two dimensional topologically ordered feature map of groups of provinces was proposed.

ACS Style

Michał Juszczyk. Self-organizing maps application for the clustering of the provinces of Poland according to the construction industry activity. INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016) 2017, 1 .

AMA Style

Michał Juszczyk. Self-organizing maps application for the clustering of the provinces of Poland according to the construction industry activity. INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). 2017; ():1.

Chicago/Turabian Style

Michał Juszczyk. 2017. "Self-organizing maps application for the clustering of the provinces of Poland according to the construction industry activity." INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016) , no. : 1.

Journal article
Published: 29 November 2016 in DEStech Transactions on Economics, Business and Management
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Cost estimation is one of the key tasks in the process of construction project management. Total costs incurred during the construction stage of the project by a contractor include direct costs that are related to works execution and indirect costs that accompany the delivery. This paper presents Artificial Neural Network (ANN) approach for prediction index of site overhead cost which is significant part of indirect cost. Applicability of Radial Basic Function (RBF) networks was investigated. A quantitative study on the factors conditioning site overhead costs of polish construction projects was completed. Moreover actual site overhead costs incurred by enterprises during project implementation were investigated. This research phase resulted in completion of a data set which covered 143 real-life cases of building projects. On the basis of the neural modelling the authors stated that the RBF networks can be a promising solution in the regression problem of site overhead cost index prediction.

ACS Style

Michał Juszczyk; Agnieszka Leśniak. Site Overhead Cost Index Prediction Using RBF Neural Networks. DEStech Transactions on Economics, Business and Management 2016, 1 .

AMA Style

Michał Juszczyk, Agnieszka Leśniak. Site Overhead Cost Index Prediction Using RBF Neural Networks. DEStech Transactions on Economics, Business and Management. 2016; (icem):1.

Chicago/Turabian Style

Michał Juszczyk; Agnieszka Leśniak. 2016. "Site Overhead Cost Index Prediction Using RBF Neural Networks." DEStech Transactions on Economics, Business and Management , no. icem: 1.

Journal article
Published: 01 January 2016 in Procedia Engineering
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ACS Style

Michał Juszczyk; Andrzej Tomana; Maja Bartoszek. Current Issues of BIM-based Design Change Management, Analysis and Visualization. Procedia Engineering 2016, 164, 518 -525.

AMA Style

Michał Juszczyk, Andrzej Tomana, Maja Bartoszek. Current Issues of BIM-based Design Change Management, Analysis and Visualization. Procedia Engineering. 2016; 164 ():518-525.

Chicago/Turabian Style

Michał Juszczyk; Andrzej Tomana; Maja Bartoszek. 2016. "Current Issues of BIM-based Design Change Management, Analysis and Visualization." Procedia Engineering 164, no. : 518-525.

Conference paper
Published: 01 January 2016 in INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015)
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The paper presents concisely some research results on the application of principal component analysis for the data compression and the use of compressed data as the variables describing the model in the issue of conceptual cost estimation of residential buildings. The goal of the research was to investigate the possibility of use of compressed input data of the model in neural modelling - the basic information about residential buildings available in the early stage of design and construction cost. The results for chosen neural networks that were trained with use of the compressed input data are presented in the paper. In the summary the results obtained for the neural networks with PCA-based data compression are compared with the results obtained in the previous stage of the research for the network committees.

ACS Style

Michał Juszczyk. Application of PCA-based data compression in the ANN-supported conceptual cost estimation of residential buildings. INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015) 2016, 1738, 200007 .

AMA Style

Michał Juszczyk. Application of PCA-based data compression in the ANN-supported conceptual cost estimation of residential buildings. INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). 2016; 1738 ():200007.

Chicago/Turabian Style

Michał Juszczyk. 2016. "Application of PCA-based data compression in the ANN-supported conceptual cost estimation of residential buildings." INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015) 1738, no. : 200007.

Journal article
Published: 01 September 2015 in Archives of Civil Engineering
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The article presents briefly several methods of working time estimation. However, three methods of task duration assessment have been selected to investigate working time in a real construction project using the data collected from observing workers laying terrazzo flooring in staircases. The first estimation has been done by calculating a normal and a triangular function. The next method, which is the focus of greatest attention here, is PERT. The article presents a way to standardize the results and the procedure algorithm allowing determination of the characteristic values for the method. Times to perform every singular component sub-task as well as the whole task have been defined for the collected data with the reliability level of 85%. The completion time of the same works has also been calculated with the use of the KNR. The obtained result is much higher than the actual time needed for execution of the task calculated with the use of the previous method. The authors argue that PERT is the best method of all three, because it takes into account the randomness of the entire task duration and it can be based on the actual execution time known from research.

ACS Style

E. Plebankiewicz; Michał Juszczyk; J. Malara. Estimation Of Task Completion Times With The Use Of The PERT Method On The Example Of A Real Construction Project. Archives of Civil Engineering 2015, 61, 51 -62.

AMA Style

E. Plebankiewicz, Michał Juszczyk, J. Malara. Estimation Of Task Completion Times With The Use Of The PERT Method On The Example Of A Real Construction Project. Archives of Civil Engineering. 2015; 61 (3):51-62.

Chicago/Turabian Style

E. Plebankiewicz; Michał Juszczyk; J. Malara. 2015. "Estimation Of Task Completion Times With The Use Of The PERT Method On The Example Of A Real Construction Project." Archives of Civil Engineering 61, no. 3: 51-62.

Conference paper
Published: 01 January 2015 in PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014)
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The paper presents concisely some research results on the application of committees of artificial neural networks (that is committees of networks also called committee machines) for conceptual cost estimation of residential buildings. The author focused on application of chosen static structure type of network committees in regression problem binding together the basic information about residential buildings available in the early stage of design and construction cost. The goal of the research was to improve the formerly proposed regression model based on a single network – especially to minimize the number of occurrences of errors with a high value. Due to the results, in the described problem of conceptual cost estimation obtained, committees of networks proved to be better solution for the regression model than a single networks. The conclusion is that a neural approach involving committees of artificial neural networks may be an alternative both for the single neural network based models and the traditional methods of conceptual cost estimation in construction projects.

ACS Style

Michał Juszczyk. Application of committees of neural networks for conceptual cost estimation of residential buildings. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014) 2015, 1648, 600008 .

AMA Style

Michał Juszczyk. Application of committees of neural networks for conceptual cost estimation of residential buildings. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014). 2015; 1648 (1):600008.

Chicago/Turabian Style

Michał Juszczyk. 2015. "Application of committees of neural networks for conceptual cost estimation of residential buildings." PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014) 1648, no. 1: 600008.