Construction duration predictive model based on factorial analysis and fuzzy logic
Setting the building construction duration for vertical residential works is made still in the study phase of economic and financial feasibility of the project and, in most cases, in an empirical way, increasing the uncertainties and the risks to fulfill the set deadline. However, there are computat...
में बचाया:
| मुख्य लेखकों: | , , , , |
|---|---|
| स्वरूप: | Online |
| भाषा: | eng |
| प्रकाशित: |
ANTAC - Associação Nacional de Tecnologia do Ambiente Construído
2019
|
| ऑनलाइन पहुंच: | https://seer.ufrgs.br/ambienteconstruido/article/view/79907 |
| टैग : |
टैग जोड़ें
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
|
| id |
ojs-article-79907 |
|---|---|
| record_format |
ojs |
| spelling |
ojs-article-799072019-10-04T13:09:11Z Construction duration predictive model based on factorial analysis and fuzzy logic Maués, Luiz Maurício Furtado Sá, José Alberto Silva de Costa Junior, Carlos Tavares da Kern, Andrea Parise Duarte, André Augusto Azevedo Montenegro Modeling. Fuzzy Logic. Building Construction. Deadline. Setting the building construction duration for vertical residential works is made still in the study phase of economic and financial feasibility of the project and, in most cases, in an empirical way, increasing the uncertainties and the risks to fulfill the set deadline. However, there are computational intelligence tools that can contribute to reduce the degree of uncertainty. This study aimed to investigate the use of a hybrid system to estimate the deadline for vertical residential building works from design and production characteristics using factorial analysis and Fuzzy Systems. To this end, we used information of a database from the SEURB and in some buildings construction companies in Belém, a city located in the State of Pará, northern of Brazil. For the training and construction of the Fuzzy Forecast Model, data from 71 projects were used and 16 others residential buildings were used for its validation. The results showed a significant level of assertiveness, with 75% accuracy considering a range, whose upper and lower limits were calculated from MAPE and MASE. The model presented a prediction performance superior to other models already consecrated in the literature. ANTAC - Associação Nacional de Tecnologia do Ambiente Construído 2019-10-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://seer.ufrgs.br/ambienteconstruido/article/view/79907 Ambiente Construído; v. 19, n. 4 (2019); 115-133 Ambiente Construído; v. 19, n. 4 (2019); 115-133 Ambiente Construído; v. 19, n. 4 (2019); 115-133 1678-8621 1415-8876 eng https://seer.ufrgs.br/ambienteconstruido/article/view/79907/54334 Direitos autorais 2019 Ambiente Construído https://creativecommons.org/licenses/by/4.0 |
| institution |
Universidade Federal do Rio Grande do Sul |
| collection |
OJS |
| language |
eng |
| format |
Online |
| author |
Maués, Luiz Maurício Furtado Sá, José Alberto Silva de Costa Junior, Carlos Tavares da Kern, Andrea Parise Duarte, André Augusto Azevedo Montenegro |
| spellingShingle |
Maués, Luiz Maurício Furtado Sá, José Alberto Silva de Costa Junior, Carlos Tavares da Kern, Andrea Parise Duarte, André Augusto Azevedo Montenegro Construction duration predictive model based on factorial analysis and fuzzy logic |
| author_facet |
Maués, Luiz Maurício Furtado Sá, José Alberto Silva de Costa Junior, Carlos Tavares da Kern, Andrea Parise Duarte, André Augusto Azevedo Montenegro |
| author_sort |
Maués, Luiz Maurício Furtado |
| title |
Construction duration predictive model based on factorial analysis and fuzzy logic |
| title_short |
Construction duration predictive model based on factorial analysis and fuzzy logic |
| title_full |
Construction duration predictive model based on factorial analysis and fuzzy logic |
| title_fullStr |
Construction duration predictive model based on factorial analysis and fuzzy logic |
| title_full_unstemmed |
Construction duration predictive model based on factorial analysis and fuzzy logic |
| title_sort |
construction duration predictive model based on factorial analysis and fuzzy logic |
| description |
Setting the building construction duration for vertical residential works is made still in the study phase of economic and financial feasibility of the project and, in most cases, in an empirical way, increasing the uncertainties and the risks to fulfill the set deadline. However, there are computational intelligence tools that can contribute to reduce the degree of uncertainty. This study aimed to investigate the use of a hybrid system to estimate the deadline for vertical residential building works from design and production characteristics using factorial analysis and Fuzzy Systems. To this end, we used information of a database from the SEURB and in some buildings construction companies in Belém, a city located in the State of Pará, northern of Brazil. For the training and construction of the Fuzzy Forecast Model, data from 71 projects were used and 16 others residential buildings were used for its validation. The results showed a significant level of assertiveness, with 75% accuracy considering a range, whose upper and lower limits were calculated from MAPE and MASE. The model presented a prediction performance superior to other models already consecrated in the literature. |
| publisher |
ANTAC - Associação Nacional de Tecnologia do Ambiente Construído |
| publishDate |
2019 |
| url |
https://seer.ufrgs.br/ambienteconstruido/article/view/79907 |
| work_keys_str_mv |
AT mauesluizmauriciofurtado constructiondurationpredictivemodelbasedonfactorialanalysisandfuzzylogic AT sajosealbertosilvade constructiondurationpredictivemodelbasedonfactorialanalysisandfuzzylogic AT costajuniorcarlostavaresda constructiondurationpredictivemodelbasedonfactorialanalysisandfuzzylogic AT kernandreaparise constructiondurationpredictivemodelbasedonfactorialanalysisandfuzzylogic AT duarteandreaugustoazevedomontenegro constructiondurationpredictivemodelbasedonfactorialanalysisandfuzzylogic |
| _version_ |
1709370670075346944 |