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Estimating the Technical Deterioration of Large-panel Residential Buildings Using Artificial Neural Networks
Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 91 (2014) 394 – 399
XXIII R-S-P seminar, Theoretical Foundation of Civil Engineering (23RSP) (TFoCE 2014)
Estimating the Technical Deterioration of Large-Panel Residential Buildings Using Artificial Neural Networks Piotr Knyziaka* a
Warsaw University of Technology, Faculty of Civil Engineering, Armii Ludowej 16, 00-637 Warsaw, Poland
Abstract In order to identify the repair needs of large housing estates a simplified method for estimating the technical deterioration of large number of houses is needed. A method presented in the paper is based on the extracted data processed by means of artificial neural networks (ANN). The aim is to create the artificial neural network configurations for a set of data containing values of the technical deterioration and information about building repairs obtained in earlier years (or other information and building parameters) and next to analyze new buildings by the instructed neural network. The profit from using ANN is the reduction of the number of parameters. Instead of more than forty parameters describing a building, usually about ten are sufficient for satisfactory accuracy. Three types of ANN are used: MLP - Multilayer Perceptron, RBF - Radial Basis Function, SVM - Support Vector Machine. The paper presents the part of results of author’s PhD dissertation [1], [2]. © 2014 2014The TheAuthors. Authors. Published by Elsevier Ltd. is an open access article under the CC BY-NC-ND license © Published by Elsevier Ltd. This Peer-review under responsibility of organizing committee of the XXIII R-S-P seminar, Theoretical Foundation of Civil (http://creativecommons.org/licenses/by-nc-nd/3.0/). Engineeringunder (23RSP). Peer-review responsibility of organizing committee of the XXIII R-S-P seminar, Theoretical Foundation of Civil Engineering (23RSP) Keywords: technical state; precast concrete; technical deterioration; large block technology; large panel technology; artificial neural networks.
1. Introduction Thousands of residential buildings made of precast elements have been built in Poland through the last fifty years. Some of the buildings were repaired and modernized but often in an insufficient degree. Nowadays this is the important issue in Poland. After years of usage, one question is asked frequently, “What is the technical deterioration of these buildings?”. This is a serious technical as well as a social problem. Many opinions were delivered about the technical state and the technical deterioration of precast blocks of flats located in Poland. They are contradictory sometimes, what is a result of using very complicated and non-repeatable methods. Assessment of the technical deterioration of a building can be performed by one of the many methods. These methods consider different number of analyzed details, require expertise, are based on expert’s reports and * Corresponding author. Tel.: +48 22 2346587; fax: + 48 22 825 65 32. E-mail address: [email protected]
1877-7058 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of organizing committee of the XXIII R-S-P seminar, Theoretical Foundation of Civil Engineering (23RSP) doi:10.1016/j.proeng.2014.12.082
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
395
their execution is time consuming. These methods show different deficiencies: labour-consumption, inaccuracy, lack of objectiveness and others. Even the most exact, but labour-consuming, “visual methods” are defective – two assessments for the same building, done by different specialists, can disagree. This is result of differences in condition assessment for a building element, particularly if it is hidden under paints, parquets, and other finishing layers. These elements can be evaluated indirectly only, through vision inspection of apparent flaws, cracks, delamination etc. There is a need for new solutions which would help to analyze current values of the technical deterioration for many buildings and would allow for prediction of the future changes. Additionally, the new method should be easier to use. It seems that until now there has been published only one work [3] dedicated to the definition of the technical state of buildings, based on artificial methods. This paper presents analysis of buildings inhibited for long time (constructed before 1918), built of small elements (bricks), often with timber structure of ceilings and roofs. The approach presented here for large-panel residential buildings is new. Many scientists are interested with the problem of the technical condition, the technical deterioration and the prediction of the lifetime of buildings (eg. [3][13]). 2. Database Data were completed from January 2005 to March 2006 in 15 housing estates in Warsaw in Poland. This paper presents result of analysis made on representative group of 95 precast residential buildings. All data was obtained from housing estates and own inspections. Housing estates are periodically examined in the general inspection, at ones every five years, and in the normal annual inspection. Data were collected in a representative quantity and quality, for residential buildings made in the large block (33 buildings) and large panel (62 buildings) technology. Number of analyzed buildings due to the year of construction (Fig. 1) in a large extent repeats the trend of the distribution of total number of buildings put into use each year (trend for Warsaw). They are good representation of all the buildings of the housing estates and describe as an overview of precast residential buildings in Warsaw. Comparison number of buildings to year of construction is presented in Fig. 2. It can be assumed that by audited buildings perspective conclusions relate directly to at least three times greater number of buildings.
Fig. 1. Number of buildings to year of construction.
Special questionnaire has been prepared for collected data. It contains over 150 elements in every record, collected as a result of analysis of engineering documentations, examining object’s books, examination protocols, additional technical expertise’s, visual inspections of elevations and inside of the building, and photographic documentation. Prepared questionnaire contains the following parts: address and characteristics of the real estate,
396
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
data collection, technical condition of the building, the surroundings of the building, construction solutions and material characteristics, technical condition - damages, frost penetration, corrosion, condition of the elements, modernization and repairs, interview with residents - manner of maintaining the building, random events, etc.
Fig. 2. Technical deterioration for buildings from database.
Data from questionnaires were imported to the MS Excel spreadsheet. Processing of the data, performing calculations, creating charts has been based on the use of formulas and functions in a spreadsheet in conjunction with significant support in the form of short utility programs written in Visual Basic for Application for MS Excel, providing automation of some tasks. Further processing, associated with the use of artificial neural networks have been carried out using NeuroSolutions application. The data input includes not only data about the technical state of buildings, but also other information such as about environment, type of maintenance, special events, repairs and modernizations. The information collected in the data base could allow performing miscellaneous analyses. In the first step in spreadsheet a preliminary analysis of the collected data has been performed, with respect of the importance of information, the amount of data and its variation. From questionnaire to the database were introduced the 158 parameters describing the buildings. After the first stage of the analysis the 42 parameters remained. In the next step it was concluded that some of the data can be grouped. Therefore it has been determined the impact of these groups on the value of the technical condition of precast residential buildings. Moreover, the importance of 16 parameters directly assessing on the technical state of the building elements was established. The correlation analysis shows correlation between the data and value of building technical condition. Finally three groups of separated parameters from database records were prepared for next works. 3. Estimating the technical deterioration of buildings using artificial neural networks A method presented below is based on the extracted data processing by means of artificial neural networks. The aim is to create the artificial neural network configurations for a set of data containing values of the technical deterioration and information about building repairs obtained in last years (or other information and building parameters) and next to analyze new buildings by the instructed neural network. The second profit from using artificial neural networks is the reduction of the number of parameters. Instead of more than forty parameters describing a building, usually about ten are sufficient for satisfactory accuracy. This method could have lower accuracy but it is less prone to data errors and more repeatable.
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
397
The algorithm for obtaining results from artificial neural networks (ANN) has been established and it consists following steps: 1. Preparing full database in MS Excel, 2. Selection of data for the next step of analysis, 3. Data normalization for use in net – changing range of values from <0, N> to <0, 1>, 4. Selecting components from the records, 5. Sorting and selecting records for groups of data: for training, for cross validation, for testing, 6. Choosing the type of artificial neural net, selecting topology, 7. Teaching the net, verifying results, 8. Repeating steps (5, 6, 7) for optimization of the network architecture, 9. Analysing new data with the optimal net. In the network modelling process were accepted following conditions as indications of high-quality network: x The correlation coefficient of the results was close to 1, x The teaching and verification errors value were low and similar values. In the network learning process were used the following criteria for termination (stop criteria): x Achieved the assumed maximum number of periods, x Validation error starts to increase.
Fig. 3. Learning process - criteria for termination.
Three types of artificial neural networks have been used for analyses: x MLP - Multilayer Perceptron, x RBF - Radial Basis Function, x SVM - Support Vector Machine As input signals to the network ware taken 10 parameters: x Year of construction, x Condition of balconies and loggias, x Condition of external plaster, x Condition of flats window woodwork, x Condition of central heating system pipes, x Condition of system pipes cold and hot water, x Condition of roof covering, x Condition of thermal insulation of curtain walls, x Condition of thermal insulation of gable walls, x Condition of thermal insulation of roof. As output signal for the network teaching was taken: x Technical deterioration by visual (expert) method.
398
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
Summary of results for different groups of parameters and different types of ANN is presented in Table 1. The net of MLP type reached the best result (with architecture 10-4-1, 10 input, 1 hidden layer with 4 neurons, 1 output, function of activation - sigmoid, gradient training method - with momentum value 0.8). Comparison of the original data to results obtained from MLP network for the best architecture is presented in Fig. 1. Table 1. Summary of results for different groups of parameters and different types of ANN. Group of Type and Minimum Minimum MSE NMSE MAE parameters net MSE MSE - Cross Testing Testing Testing number Training Validation I
II
III
Min Abs Error
Max Abs Error
- Testing
- Testing
R-linear correlation coefficient
MLP-05
2.63
1.99
0.30
34.4
13.6
0.74
30.4
0.9873
RBF-14
9.43
4.74
0.51
58.7
19.8
1.59
35.6
0.9852
SVM-01
24.51
35.20
3.48
403.2
50.5
3.89
111.9
0.8946
MLP-03
27.14
25.03
6.53
490.4
68.8
5.53
143.5
0.7755
RBF-02
30.17
24.57
5.50
413.2
60.5
3.62
180.5
0.7712
SVM-01
60.79
32.40
16.23
1220.1
99.3
2.24
323.0
0.4106
MLP-03
45.97
55.02
6.53
491.0
70.0
2.71
159.9
0.7879
RBF-14
50.25
64.78
6.97
524.1
67.0
0.29
174.4
0.7773
SVM-01
105.99
109.12
11.52
865.9
93.9
18.66
179.7
0.4556
MSE - root mean square error, NMSE - MSE / variance), MAE - mean absolute error, Min / Max Abs Error - the smallest / largest absolute error, the value of "r"-the correlation coefficient.
Fig. 4. Comparison the original data to results obtained from network.
The ANN sensitivity analysis led to obtain a set of five parameters affecting the greatest technical assessment of buildings. The obtained parameters depend on conditions of: x flats window woodwork,
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
399
x system pipes cold and hot water, x roof covering, x thermal insulation of curtain walls, x thermal insulation of gable walls. These five parameters significantly affect the value of technical deterioration of the building. In the approximate evaluations primarily these parameters should be taken into special consideration. 4. Summary Good accuracy of the results was obtained. Basically for large groups of buildings, this method could be used directly. It should be remembered that this is an approximate method. The results are more objective, because of a reduced impact of evaluations made by inspectors. Estimating the technical deterioration of building using artificial neural networks: x Has lower accuracy, x Can work on incomplete data, x Is more resistant to errors in data, x Needs small range of data as an input, x The Multilayer Perceptron is type of the net, which gives good results, the error is not significant, x It can be applied as one from many methods for estimating technical deterioration. Proposed method could be used on many ways. Housing estates could use this method for large groups of buildings, it is cheap and quick method for estimating the technical deterioration for preparing financial plans of repairs, simulations of repairs plans. This method could help to define the technical value of buildings, in settlement of fees insurances and cadastral tax which is foreseen to introduction. References [1] P. Knyziak, Analysis of the Technical State of Large-Panel Residential Buildings Using Artificial Neural Networks (in Polish), PhD thesis. Warsaw University of Technology, Warsaw 2007. [2] P. Knyziak, M. Witkowski, Estimation of the Technical State for Large-Panel Residential Buildings in Warsaw (in Polish), InĪynieria i Budownictwo, 12/2007, pp. 639-641. [3] Z. Waszczyszyn, P. UrbaĔski, Neural Prediction of the Degree of Technical Deterioration of Residential Buildings (in Polish), XLVIII Konferencja Naukowa KILiW PAN i KN PZITB, Opole-Krynica, pp. 365-372, 2002. [4] B. Lewicki, Evaluation Methodology of the Technical Condition of the Large-Panel Buildings (in Polish), Building Research Institute, Warsaw 2002. [5] L. Runkiewicz, J. SzymaĔski, Damages and Risks Occurring in Large-Panel Residential Buildings (in Polish), V Conference „Warsztat Pracy Rzeczoznawcy Budowlanego”, Kielce 1999. [6] Technical Deterioration of Buildings (in Polish), WACETOB, Warsaw 2000. [7] L. Czarnecki, P. Woyciechowski, Concrete Carbonation as a Limited Process and its Relevance to Concrete Cover Thickness, ACI Materials Journal, 3 (109) 2012 1-8, pp. 275-282. [8] R. Foliü, Durability Design of Concrete Structures - Part 1: Analysis Fundamentals, Facta Universitatis, Series: Architecture and Civil Engineering Vol. 7, No 1, 2009, pp. 1 – 18. [9] R. Foliü, D. Zenunoviü, Durability Design of Concrete Structures - Part 2: Modelling And Structural Assessment, Facta Universitatis, Series: Architecture and Civil Engineering Vol. 8, No 1, 2010, pp. 45 – 66. [10] C. A. Balaras, K. Droutsa, E. Dascalaki, S. Kontoyiannidis, Deterioration of European Apartment Buildings, Energy and Buildings, 37 (2005) 515–527. [11] M. Bauer, J. Lair, C. Wetzel, WP2 Final Technical Report: Predictive Model for Future Deterioration, INVESTIMMO Project, European Commission, 2004. [12] C.A. Balaras, E. Dascalaki, K. Droutsa, S. Kontoyiannidis, European Residential Building Audits Database—ERBAD Multimedia CDROM, Group Energy Conservation, IERSD, National Observatory of Athens, Athens, March 2004, ISBN: 960-87905-5-7. [13] ACI Committee 365. 1R-42: Service-Life Prediction-State of the Art Report, 2000.
ScienceDirect Procedia Engineering 91 (2014) 394 – 399
XXIII R-S-P seminar, Theoretical Foundation of Civil Engineering (23RSP) (TFoCE 2014)
Estimating the Technical Deterioration of Large-Panel Residential Buildings Using Artificial Neural Networks Piotr Knyziaka* a
Warsaw University of Technology, Faculty of Civil Engineering, Armii Ludowej 16, 00-637 Warsaw, Poland
Abstract In order to identify the repair needs of large housing estates a simplified method for estimating the technical deterioration of large number of houses is needed. A method presented in the paper is based on the extracted data processed by means of artificial neural networks (ANN). The aim is to create the artificial neural network configurations for a set of data containing values of the technical deterioration and information about building repairs obtained in earlier years (or other information and building parameters) and next to analyze new buildings by the instructed neural network. The profit from using ANN is the reduction of the number of parameters. Instead of more than forty parameters describing a building, usually about ten are sufficient for satisfactory accuracy. Three types of ANN are used: MLP - Multilayer Perceptron, RBF - Radial Basis Function, SVM - Support Vector Machine. The paper presents the part of results of author’s PhD dissertation [1], [2]. © 2014 2014The TheAuthors. Authors. Published by Elsevier Ltd. is an open access article under the CC BY-NC-ND license © Published by Elsevier Ltd. This Peer-review under responsibility of organizing committee of the XXIII R-S-P seminar, Theoretical Foundation of Civil (http://creativecommons.org/licenses/by-nc-nd/3.0/). Engineeringunder (23RSP). Peer-review responsibility of organizing committee of the XXIII R-S-P seminar, Theoretical Foundation of Civil Engineering (23RSP) Keywords: technical state; precast concrete; technical deterioration; large block technology; large panel technology; artificial neural networks.
1. Introduction Thousands of residential buildings made of precast elements have been built in Poland through the last fifty years. Some of the buildings were repaired and modernized but often in an insufficient degree. Nowadays this is the important issue in Poland. After years of usage, one question is asked frequently, “What is the technical deterioration of these buildings?”. This is a serious technical as well as a social problem. Many opinions were delivered about the technical state and the technical deterioration of precast blocks of flats located in Poland. They are contradictory sometimes, what is a result of using very complicated and non-repeatable methods. Assessment of the technical deterioration of a building can be performed by one of the many methods. These methods consider different number of analyzed details, require expertise, are based on expert’s reports and * Corresponding author. Tel.: +48 22 2346587; fax: + 48 22 825 65 32. E-mail address: [email protected]
1877-7058 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of organizing committee of the XXIII R-S-P seminar, Theoretical Foundation of Civil Engineering (23RSP) doi:10.1016/j.proeng.2014.12.082
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
395
their execution is time consuming. These methods show different deficiencies: labour-consumption, inaccuracy, lack of objectiveness and others. Even the most exact, but labour-consuming, “visual methods” are defective – two assessments for the same building, done by different specialists, can disagree. This is result of differences in condition assessment for a building element, particularly if it is hidden under paints, parquets, and other finishing layers. These elements can be evaluated indirectly only, through vision inspection of apparent flaws, cracks, delamination etc. There is a need for new solutions which would help to analyze current values of the technical deterioration for many buildings and would allow for prediction of the future changes. Additionally, the new method should be easier to use. It seems that until now there has been published only one work [3] dedicated to the definition of the technical state of buildings, based on artificial methods. This paper presents analysis of buildings inhibited for long time (constructed before 1918), built of small elements (bricks), often with timber structure of ceilings and roofs. The approach presented here for large-panel residential buildings is new. Many scientists are interested with the problem of the technical condition, the technical deterioration and the prediction of the lifetime of buildings (eg. [3][13]). 2. Database Data were completed from January 2005 to March 2006 in 15 housing estates in Warsaw in Poland. This paper presents result of analysis made on representative group of 95 precast residential buildings. All data was obtained from housing estates and own inspections. Housing estates are periodically examined in the general inspection, at ones every five years, and in the normal annual inspection. Data were collected in a representative quantity and quality, for residential buildings made in the large block (33 buildings) and large panel (62 buildings) technology. Number of analyzed buildings due to the year of construction (Fig. 1) in a large extent repeats the trend of the distribution of total number of buildings put into use each year (trend for Warsaw). They are good representation of all the buildings of the housing estates and describe as an overview of precast residential buildings in Warsaw. Comparison number of buildings to year of construction is presented in Fig. 2. It can be assumed that by audited buildings perspective conclusions relate directly to at least three times greater number of buildings.
Fig. 1. Number of buildings to year of construction.
Special questionnaire has been prepared for collected data. It contains over 150 elements in every record, collected as a result of analysis of engineering documentations, examining object’s books, examination protocols, additional technical expertise’s, visual inspections of elevations and inside of the building, and photographic documentation. Prepared questionnaire contains the following parts: address and characteristics of the real estate,
396
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
data collection, technical condition of the building, the surroundings of the building, construction solutions and material characteristics, technical condition - damages, frost penetration, corrosion, condition of the elements, modernization and repairs, interview with residents - manner of maintaining the building, random events, etc.
Fig. 2. Technical deterioration for buildings from database.
Data from questionnaires were imported to the MS Excel spreadsheet. Processing of the data, performing calculations, creating charts has been based on the use of formulas and functions in a spreadsheet in conjunction with significant support in the form of short utility programs written in Visual Basic for Application for MS Excel, providing automation of some tasks. Further processing, associated with the use of artificial neural networks have been carried out using NeuroSolutions application. The data input includes not only data about the technical state of buildings, but also other information such as about environment, type of maintenance, special events, repairs and modernizations. The information collected in the data base could allow performing miscellaneous analyses. In the first step in spreadsheet a preliminary analysis of the collected data has been performed, with respect of the importance of information, the amount of data and its variation. From questionnaire to the database were introduced the 158 parameters describing the buildings. After the first stage of the analysis the 42 parameters remained. In the next step it was concluded that some of the data can be grouped. Therefore it has been determined the impact of these groups on the value of the technical condition of precast residential buildings. Moreover, the importance of 16 parameters directly assessing on the technical state of the building elements was established. The correlation analysis shows correlation between the data and value of building technical condition. Finally three groups of separated parameters from database records were prepared for next works. 3. Estimating the technical deterioration of buildings using artificial neural networks A method presented below is based on the extracted data processing by means of artificial neural networks. The aim is to create the artificial neural network configurations for a set of data containing values of the technical deterioration and information about building repairs obtained in last years (or other information and building parameters) and next to analyze new buildings by the instructed neural network. The second profit from using artificial neural networks is the reduction of the number of parameters. Instead of more than forty parameters describing a building, usually about ten are sufficient for satisfactory accuracy. This method could have lower accuracy but it is less prone to data errors and more repeatable.
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
397
The algorithm for obtaining results from artificial neural networks (ANN) has been established and it consists following steps: 1. Preparing full database in MS Excel, 2. Selection of data for the next step of analysis, 3. Data normalization for use in net – changing range of values from <0, N> to <0, 1>, 4. Selecting components from the records, 5. Sorting and selecting records for groups of data: for training, for cross validation, for testing, 6. Choosing the type of artificial neural net, selecting topology, 7. Teaching the net, verifying results, 8. Repeating steps (5, 6, 7) for optimization of the network architecture, 9. Analysing new data with the optimal net. In the network modelling process were accepted following conditions as indications of high-quality network: x The correlation coefficient of the results was close to 1, x The teaching and verification errors value were low and similar values. In the network learning process were used the following criteria for termination (stop criteria): x Achieved the assumed maximum number of periods, x Validation error starts to increase.
Fig. 3. Learning process - criteria for termination.
Three types of artificial neural networks have been used for analyses: x MLP - Multilayer Perceptron, x RBF - Radial Basis Function, x SVM - Support Vector Machine As input signals to the network ware taken 10 parameters: x Year of construction, x Condition of balconies and loggias, x Condition of external plaster, x Condition of flats window woodwork, x Condition of central heating system pipes, x Condition of system pipes cold and hot water, x Condition of roof covering, x Condition of thermal insulation of curtain walls, x Condition of thermal insulation of gable walls, x Condition of thermal insulation of roof. As output signal for the network teaching was taken: x Technical deterioration by visual (expert) method.
398
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
Summary of results for different groups of parameters and different types of ANN is presented in Table 1. The net of MLP type reached the best result (with architecture 10-4-1, 10 input, 1 hidden layer with 4 neurons, 1 output, function of activation - sigmoid, gradient training method - with momentum value 0.8). Comparison of the original data to results obtained from MLP network for the best architecture is presented in Fig. 1. Table 1. Summary of results for different groups of parameters and different types of ANN. Group of Type and Minimum Minimum MSE NMSE MAE parameters net MSE MSE - Cross Testing Testing Testing number Training Validation I
II
III
Min Abs Error
Max Abs Error
- Testing
- Testing
R-linear correlation coefficient
MLP-05
2.63
1.99
0.30
34.4
13.6
0.74
30.4
0.9873
RBF-14
9.43
4.74
0.51
58.7
19.8
1.59
35.6
0.9852
SVM-01
24.51
35.20
3.48
403.2
50.5
3.89
111.9
0.8946
MLP-03
27.14
25.03
6.53
490.4
68.8
5.53
143.5
0.7755
RBF-02
30.17
24.57
5.50
413.2
60.5
3.62
180.5
0.7712
SVM-01
60.79
32.40
16.23
1220.1
99.3
2.24
323.0
0.4106
MLP-03
45.97
55.02
6.53
491.0
70.0
2.71
159.9
0.7879
RBF-14
50.25
64.78
6.97
524.1
67.0
0.29
174.4
0.7773
SVM-01
105.99
109.12
11.52
865.9
93.9
18.66
179.7
0.4556
MSE - root mean square error, NMSE - MSE / variance), MAE - mean absolute error, Min / Max Abs Error - the smallest / largest absolute error, the value of "r"-the correlation coefficient.
Fig. 4. Comparison the original data to results obtained from network.
The ANN sensitivity analysis led to obtain a set of five parameters affecting the greatest technical assessment of buildings. The obtained parameters depend on conditions of: x flats window woodwork,
Piotr Knyziak / Procedia Engineering 91 (2014) 394 – 399
399
x system pipes cold and hot water, x roof covering, x thermal insulation of curtain walls, x thermal insulation of gable walls. These five parameters significantly affect the value of technical deterioration of the building. In the approximate evaluations primarily these parameters should be taken into special consideration. 4. Summary Good accuracy of the results was obtained. Basically for large groups of buildings, this method could be used directly. It should be remembered that this is an approximate method. The results are more objective, because of a reduced impact of evaluations made by inspectors. Estimating the technical deterioration of building using artificial neural networks: x Has lower accuracy, x Can work on incomplete data, x Is more resistant to errors in data, x Needs small range of data as an input, x The Multilayer Perceptron is type of the net, which gives good results, the error is not significant, x It can be applied as one from many methods for estimating technical deterioration. Proposed method could be used on many ways. Housing estates could use this method for large groups of buildings, it is cheap and quick method for estimating the technical deterioration for preparing financial plans of repairs, simulations of repairs plans. This method could help to define the technical value of buildings, in settlement of fees insurances and cadastral tax which is foreseen to introduction. References [1] P. Knyziak, Analysis of the Technical State of Large-Panel Residential Buildings Using Artificial Neural Networks (in Polish), PhD thesis. Warsaw University of Technology, Warsaw 2007. [2] P. Knyziak, M. Witkowski, Estimation of the Technical State for Large-Panel Residential Buildings in Warsaw (in Polish), InĪynieria i Budownictwo, 12/2007, pp. 639-641. [3] Z. Waszczyszyn, P. UrbaĔski, Neural Prediction of the Degree of Technical Deterioration of Residential Buildings (in Polish), XLVIII Konferencja Naukowa KILiW PAN i KN PZITB, Opole-Krynica, pp. 365-372, 2002. [4] B. Lewicki, Evaluation Methodology of the Technical Condition of the Large-Panel Buildings (in Polish), Building Research Institute, Warsaw 2002. [5] L. Runkiewicz, J. SzymaĔski, Damages and Risks Occurring in Large-Panel Residential Buildings (in Polish), V Conference „Warsztat Pracy Rzeczoznawcy Budowlanego”, Kielce 1999. [6] Technical Deterioration of Buildings (in Polish), WACETOB, Warsaw 2000. [7] L. Czarnecki, P. Woyciechowski, Concrete Carbonation as a Limited Process and its Relevance to Concrete Cover Thickness, ACI Materials Journal, 3 (109) 2012 1-8, pp. 275-282. [8] R. Foliü, Durability Design of Concrete Structures - Part 1: Analysis Fundamentals, Facta Universitatis, Series: Architecture and Civil Engineering Vol. 7, No 1, 2009, pp. 1 – 18. [9] R. Foliü, D. Zenunoviü, Durability Design of Concrete Structures - Part 2: Modelling And Structural Assessment, Facta Universitatis, Series: Architecture and Civil Engineering Vol. 8, No 1, 2010, pp. 45 – 66. [10] C. A. Balaras, K. Droutsa, E. Dascalaki, S. Kontoyiannidis, Deterioration of European Apartment Buildings, Energy and Buildings, 37 (2005) 515–527. [11] M. Bauer, J. Lair, C. Wetzel, WP2 Final Technical Report: Predictive Model for Future Deterioration, INVESTIMMO Project, European Commission, 2004. [12] C.A. Balaras, E. Dascalaki, K. Droutsa, S. Kontoyiannidis, European Residential Building Audits Database—ERBAD Multimedia CDROM, Group Energy Conservation, IERSD, National Observatory of Athens, Athens, March 2004, ISBN: 960-87905-5-7. [13] ACI Committee 365. 1R-42: Service-Life Prediction-State of the Art Report, 2000.