Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 4
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

More than 6 billion square metres of new buildings are built each year. This is about 1.2 million buildings. If we translate these figures into carbon footprint (CF) generated during the construction, it will be approximately 3.7 billion tons of carbon dioxide. The contractors all over the world – also in Poland – decide to calculate the carbon footprint for various reasons, but mostly they are compelled to do so by the market. The analysis of costs and emissions of greenhouse gases for individual phases of the construction system allows implementing solutions and preventing a negative impact on the environment without increasing the construction costs. The share of each phase in the amount of produced carbon for construction and use of the building depends mainly on the used materials and applied design solutions. Hence, the materials and solutions with lesser carbon footprint should be used. It can be achieved by using natural materials or materials which do not need much energy to be produced. The author will attempt to outline this idea and present examples of integrated analysis of costs and amount of carbon footprint during the building lifecycle.
Go to article

Authors and Affiliations

Krzysztof Zima
1
ORCID: ORCID

  1. DSc., PhD., Eng., Prof. CUT, Cracow University of Technology, Faculty of Civil Engineering, ul. Warszawska 24, 31-155 Krakow, Poland
Download PDF Download RIS Download Bibtex

Abstract

The cost estimation at the pre-project stage provides an important decision-making indicator for the future of the project. With a preliminary cost estimation, project participants can make financial decisions and cost control. The aim of this paper is to propose a model for estimating the costs of facade systems before the pre-design stage, using the GAM (Generalized Additive Model) method. The commonly used method for the valuation of facade systems is based on individual calculation. Such valuation process is complicated and time consuming. For this reason the search for a new forecasting method is justified. The database developed for modelling purposes includes 61 cases of real costs of system façade execution for public buildings. Each case is described by 16 parameters (namely, input variables). The average absolute percentage error (MAPE) was used to assess the model, which takes the value of 14,26% for the generalized model with a logarithmic binding function and 11.77% for the model with an identity binding function. On the basis of the studies and the results obtained, it can be concluded that the constructed model is useful and can improve the process of forecasting system façade costs at the pre-projection stage.
Go to article

Bibliography


[1] D. A. Aczel, “Statystyka w Zarządzaniu”, Wydawnictwo Naukowe PWN, 2017.
[2] H. Anysz, „Managing Delays in Construction Projects Aiming at Cost Overrun Minimization”, In IOP Conference Series: Materials Science and Engineering, Vol. 603, No. 3, 2004, 2019. https://doi.org/10.1088/1757-899X/603/3/032004
[3] A. Belusic, I. Herceg-Bulic, Z. Bencetic Klaic, “Using a generalized additive model to quantify the influence of local meterology on air quality in Zagreb”, Geofizyka, Vol. 32, No. 5, pp. 47–77, 2015. https://doi.org/10.15233/gfz.2015.32.5
[4] K. Coussement, D. F. Benoit, D. Van den Poel, “Improved marketing decision making in a customer churn prediction context using generalized additive models”, Expert Systems with Applications, Vol. 37, No. 3, pp. 2132–2143, 2009.
[5] M. S. El-Abbasy, t. Zayed, “Generic scheduling optimization model for multiple construction projects”, Journal of computing in civil engineering, Vol. 31, No. 4, 04017003, 2017. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000659
[6] M. Górka, „Use of aluminium and glass facades in urban architecture”, Budownictwo i Architektura, Vol. 18, No. 3, pp. 29–40, 2019. https://doi.org/10.35784/bud-arch.586
[7] T. J. Hastii, R. J. Tibshirani, „Generalized Additive Models”, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 1990.
[8] M. Juszczyk, “Residential buildings conceptual cost estimates with the use of support vector regression”, In MATEC Web of Conferences, Vol. 196, 04090, 2018.
[9] M. Juszczyk, K. Zima, W. Lelek, „Forecasting of sports fields construction costs aided by ensembles of neural networks”, Journal of Civil Engineering and Management, Vol. 25, No. 7, pp. 715–729, 2019. https://doi.org/10.3846/jcem.2019.10534
[10] P. Kamble, N. Sanadi, “Optimization of Time and Cost of Building Construction using Fast Tracking Method of Scheduling”, Optimization, Vol. 6, No. 07, 2019.
[11] O. Kapliński, “Problematyka inżynierii przedsięwzięć budowlanych na konferencjach krynickich 2017 i 2018”, Przegląd Budowlany, 89, 2018.
[12] T. Kasprowicz, „Inżynieria przedsięwzięć budowlanych. Metody i modele w Inżynierii przedsięwzięć budowlanych”, Pr. zb. pod red. Kapliński Oleg, PAN KILiW, IPPT, 2007.
[13] M. Kozlovska, M. Spisakova, D. Mackova, „Identifying the construction waste types relating to modern methods of construction”, Book Series: International Multidisciplinary Scientific GeoConference SGEM, pp. 129–136, 2016. https://doi.org/10.5593/SGEM2016/B62/S26.018
[14] M. Krzemiński, “Optimization of work schedules executed using the flow shop model, assuming multitasking performed by work crews”, Archives of Civil Engineering, Vol. 63, No. 4, pp. 3–19, 2017. https://doi.org/10.1515/ace-2017-0037
[15] A. Kylili, P.A. Fokaides, “Policy trends for the sustainability assessment of construction materials: A review”, Sustainable Cities and Society, Vol. 35, pp. 280–288, 2017. https://doi.org/10.1016/j.scs.2017.08.013
[16] A. Leśniak, M. Górka, “Analysis of the cost structure of aluminum and glass facades”, In Advances and Trends in Engineering Sciences and Technologies III: Proceedings of the 3rd International Conference on Engineering Sciences and Technologies (ESaT 2018), CRC Press, 445, 2019. https://doi.org/10.1201/9780429021596-70
[17] A. Leśniak, M. Górka, D. Wieczorek, „Identification of factors shaping tender prices for lightweight”, Scientific Review Engineering and Environmental Sciences, Vol. 2, pp. 171–182, 2019. https://doi.org/10.22630/PNIKS.2019.28.2.16
[18] A. Leśniak, F. Janowiec. “Risk Assessment of Additional Works in Railway Construction Investments Using the Bayes Network”, Sustainability, Vol. 11, No. 19, pp. 53–88, 2019. https://doi.org/10.3390/su11195388
[19] A. Leśniak, M. Juszczyk, “Prediction of site overhead costs with the use of artificial neural network based model”, Archives of Civil and Mechanical Engineering, Vol. 18, No. 3, pp. 973–982, 2018. https://doi.org/10.1016/j.acme.2018.01.014
[20] A. Leśniak, M. Juszczyk, G. Piskorz, „Modelling Delays in Bridge Construction Projects Based on the Logit and Probit Regression”, Archives of Civil Engineering, Vol. 65, No. 2, pp. 107–120, 2019. http://doi.org/10.2478/ace-2019-0022
[21] A. Leśniak, K. Zima, „Cost calculation of construction projects including sustainability factors using the Case Based Reasoning (CBR) method”, Sustainability, Vol. 10, No. 5, 1608, 2018. https://doi.org/10.3390/su10051608
[22] P. Mccullagh, J.A. Nelder, “Generalized Linear models”, Chapman and Hall, 1989.
[23] M. Mrówczyńska, M. Sztubecka, M. Skiba, A. Bazan-Krzywoszańska, P. Bejga, „The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy”, Sustainbility, Vol. 11, No. 15, 4199, 2019. https://doi.org/10.3390/su11154199
[24] T. Nivea, T. Anu V, “Regression modelling for prediction of construction cost and duration”, In: Applied Mechanics and Materials. Trans Tech Publications, pp. 195–199, 2017. https://doi.org/10.4028/www.scientific.net/AMM.857.195
[25] B. Nowogońska, J. Korentz, “Value of Technical Wear and Costs of Restoring Performance Characteristics to Residential Buildings”, Buildings, Vol. 10, No. 1, pp. 2075–5309, 2020. https://doi.org/10.3390/buildings10010009
[26] A. Oke, C. Aigbavboa, E. Dlamini, “Factors Affecting Quality of Construction Projects in Swazilland”, In Conference: Conference: 9th International Conference on Construction in the 21st Century, At Dubai, UAE, 2017.
[27] A. Panwar, K.N. Jha, “A many-objective optimization model for construction scheduling”, Construction management and economics, Vo. 37, No. 12, pp. 727–739, 2019. https://doi.org/10.1080/01446193.2019.1590615
[28] Z. Rachid, B. Toufik, B. Mohammed, “Causes of schedule delays in construction projects in Algeria”, International Journal of Construction Management, Vol. 19, No. 5, pp. 371–381, 2019. https://doi.org/10.1080/15623599.2018.1435234
[29] M. Rogalska, “Prognozowanie rzeczywistego zużycia mieszanki betonowej do wykonania ścian szczelinowych metodą uogólnionych modeli addytywnych GAM”, Materiały Budowalne, Vol. 6, pp. 88–89, 2016.
[30] M. Rogalska, „Wieloczynnikowe modele w prognozowaniu czasu procesów budowlanych”, Politechnika Lubelska, 2016. https://doi.org/10.15199/33.2016.06.38
[31] M. Rogalska, P. Wolski, „Prognozowanie ceny 1m2 mieszkania na rynku pierwotnym w warszawie metodą uogólnionych modeli addytywnych”, Logistyka, Vol. 6, pp. 9101–9110, 2014.
[32] M. Saeedi, “Study the Effects of Constructions New Techniques and Technologies on Time, Cost and Quality of Construction Projects from the Perspective of Construction Management”, Journal of Civil Engineering and Materials Application, Vol. 1, No. 2, pp. 61–76, 2017.
[33] S.S. Shaikh, M.M. Darade, “Key performance indicator for measuring and improving quality of construction projects”, International Research Journal of Engineering and Technology (IRJET), Vol. 4, No. 5, pp. 2133–2139, 2017.
[34] D. Skorupka, A. Duchaczek, M. Kowacka, P. Zagrodnik, „Quantification of geodetic risk factors occurring at the construction project preparation stage”, Archives of Civil Engineering, Vol. 64, No. 3, pp. 195–200, 2018. https://doi.org/10.2478/ace-2018-0039
[35] M. Sztubecka, M. Skiba, M. Mrówczyńska, A. Bazan-Krzywoszańska, „An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas”, Remote Sensing, Vol. 12, No. 2, 259, 2020. https://doi.org/10.3390/rs12020259
[36] Y. Wang, B. Yu, J. Wei, F. Li, „Direct numerical simulation on drag-reducing flow by polymer additives using a spring-dumbbell model”, Progress in Computational Fliud Dynamics, an International Journal, Vol. 9, 2009. https://doi.org/10.1504/PCFD.2009.024822
[37] D. Wieczorek, E. Plebankiewicz, K. Zima, „Model estimation of the whole life cost of a building with respect to risk factors”, Technological and Economic Development of Economy, Vol. 25 No. 1, pp. 20–38, 2019. https://doi.org/10.3846/tede.2019.7455
Go to article

Authors and Affiliations

Agnieszka Leśniak
1
ORCID: ORCID
Monika Górka
1
ORCID: ORCID

  1. Cracow University of Technology, Faculty of Civil Engineering, Institute, 24 Warszawska street, 31-155 Cracow, Poland
Download PDF Download RIS Download Bibtex

Abstract

Cost estimation in the pre-design phase both for the contractor as well as the investor is an important aspect from the point of view of budget planning for a construction project. Constantly growing commercial market, especially the one of public utility constructions, makes the contractor, at the stage of development the design concept, initially estimate the cost of the facade, e.g. office buildings, commercial buildings, etc., which are most often implemented in the form of aluminum-glass facades or ventilated elevations. The valuation of facade systems is of an individual calculation nature, which makes the process complicated, time-consuming, and requiring a high cost estimation work. The authors suggest using a model for estimating the cost of facade systems with the use of statistical methods based on multiple and stepwise regression. The data base used to form statistical models is the result of quantitative-qualitative research of the design and cost documentation of completed public facilities. Basing on the obtained information, the factors that shape the costs of construction façade systems were identified; which then constitute the input variables to the suggested cost estimation models.

Go to article

Authors and Affiliations

Agnieszka Leśniak
ORCID: ORCID
Damian Wieczorek
ORCID: ORCID
Monika Górka
ORCID: ORCID
Download PDF Download RIS Download Bibtex

Abstract

The analysis of the costs and emissions of greenhouse gases for individual phases of construction investments allows for the implementation of solutions and the prevention of negative environmental impacts without significantly increasing construction costs. The share of individual investment phases in the amount of carbon dioxide (CO2) produced for the construction and use of buildings depends mainly on the materials used and the implemented design solutions. In accordance with the idea of sustainable construction, materials and design solutions with the lowest possible carbon footprint should be used. This can be achieved by using natural building materials, materials subjected to appropriate chemical composition modifications, or materials in which their production does not require large amounts of energy. The aim of the article is to determine the value of the purchase costs of selected road materials (concrete paving blocks, cement-sand bedding, concrete curbs, semi-dry concrete and concrete underlay, washed sand, and crushed aggregate with a fraction of 0–31.5 mm) for the implementation of a road investment. In addition, the authors focused on determining the size of the embodied carbon footprint due to GHG (greenhouse gas) emissions and GHG removals in a product system, expressed as CO2 equivalents for the same materials that were subjected to cost analyzes. The article presents the results of original analyzes, and indicates the optimal solutions in terms of minimizing the cost of purchasing road materials and minimizing the carbon footprint. The discussion also covers the issue of changing the chemical composition in the context of the potential impact on the reduction of material costs and CO2 equivalent emissions.
Go to article

Authors and Affiliations

Damian Wieczorek
1
ORCID: ORCID
Krzysztof Zima
1
ORCID: ORCID

  1. Cracow University of Technology, Faculty of Civil Engineering, Warszawska 24, 31-155 Kraków, Poland

This page uses 'cookies'. Learn more