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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected]

Volume 3, Issue 1, January – February 2014 ISSN 2278-6856

Volume 3, Issue 1 January – February 2014 16

Abstract: Architectural heritage represent a witness to the nation’s civilizations and its history and is considered a strong element to confirm the identity of the peoples and their their privacy. But sadly, Architectural heritage exposed to many of dangers and environmental pressures that lead to its destructions, distortion and then reduce its life time. Architectural heritage suffer from a lack of sufficient researches, studies and severe neglect by governments and organizations. In addition lack of awareness and perception the importance of heritage buildings as architectural and cultural symbols. As well as other factors such as lack of maintenance, demolition, sabotage and misuse. The main objective of this research is to study, analysis of geographic factors and their impact on heritage buildings by simulating the Architectural heritage environment. According According this study, Architectural heritage life time can be predicted. This paper presents a design framework for the integration of two new developments in this area, Architectural Information Systems and Rough Neural Networks. Paper approach involves integration between different information technologies GIS, 3D Virtual Reality and Rough Neural Networks at two phases. First phase, a 3D Virtual Reality stage and the integration of the 3D model into a 3D GIS for further management and analysis. Second Phase, represent Rough Neural Networks to build the prediction model to predict remaining time of heritage building in the near future depending on the resulting data from first Phase. This integration demonstrates the potential of Rough Neural Networks, GIS and 3D Virtual Reality as emerging tools in acquiring and analysing spatial and attributes data to improve accuracy of predictive value. General Terms: Architectural Information Systems (AIS), Artificial Neural Networks (ANNs). Keywords: GIS, 3D Virtual Reality, APIS, Architectural Heritage, Corrosion, RNNs, GINS. 1. INTRODUCTION Architectural Cultural heritage in a wider sense of the word, represent artistic, cultural and historic treasures created and handed down for centuries from generation to generation. The heritage, in all its forms, testifies to human experience and aspirations. Heritage is whatever (the natural, the built, people, traditions, and value

systems) each one of us individually or collectively (through community, region and the world) wish to preserve and pass on to the next generation. The heritage is the instrument of a two-way process between past, present and future. As a receptacle of memory, it embodies the symbolic value of cultural identities and constitutes a fundamental reference for structuring society. Insofar as it enables us to understand ourselves, the cultural heritage is one of the keys to understanding others [1]. Egypt is rightly proud of its rich history with its diverse cultural experiences and traditions, and especially its many wonderful architectural heritage assets. However, now more than ever, the combination of environmental threats such as climate change and socio-economic pressures put Architectural heritage in danger. Climate change and atmospheric pollution pose serious threats to historic buildings and monuments [2]. In recent years, there have been major changes in both the sources and amounts of emissions of air pollution that have altered the rate and extent of heritage buildings damage. Air pollutants, together with climatic parameters, are of major importance for the deterioration of many materials used in heritage buildings and monuments [3]. The impact of air pollution on architectural heritage materials is a serious concern because it can lead to loss of important parts of our history and culture. Damage includes corrosion, bio-degradation and soiling. Atmospheric corrosion is one of most dangerous harm caused by air pollution as the most serious and influential on heritage buildings. Atmospheric corrosion of heritage buildings is a complex phenomenon that is related to several factors. Atmospheric corrosion damages sometimes discovered too late. The architectural heritage are under the risk of corrosion and critical air pollution level exposure caused by chemical reactions under the multi-pollutant situation of the air pollutants, as well as meteorological factors, e.g. humidity and temperature [4].

Integration between Geo-Information Neural Systems and Architectural Information Systems

for Predicting Remaining Life of Heritage Buildings Assets

1Hana Mohamed, 2Ahmed Abou E-lfetouh Saleh, 3Sherif Barakat, 4Mona Gamal

Mansoura University, Faculty of Computer Science and Information Systems, Mansoura, Egypt Information Systems Department



Volume 3, Issue 1 January – February 2014 Page 117

Conservation of historic buildings requires comprehensive and correct information of buildings to be analysed in conservation decision making process in a systematic and rational approach [5]. Although 2-dimensional drawings and graphics provide traditional support documents to rebuild historical architecture. They are not complete solutions to detailing spatial information without truly 3-dimensional model. Architectural heritage needs more advanced techniques to support preservation and conversation in architectonic cultural heritage [6]. Conservation decision making process of historic buildings is a process that necessitates the utilization of spatial and attributes data considering different aspects of historic building and coming from different sources. At this point Geographical Information Systems (GIS), as systems developed to deal with complex and multifaceted geographical / spatial data, can be considered as an important supporting tool throughout this process. GIS can be defined as a spatial data processing system with three important components: spatial database, analytical functionality, and visualization capability. Geographical Information Systems (GIS) are advantageous in such cases which can be defined as computer based systems for handling geographical and spatial data. GIS have the potential to support the conservation decision making process with their storing, analysing and monitoring capabilities. Therefore, information systems like GIS can be seen as a potential significant instrument for dealing with the architectonic heritage conservation projects [5]. Geographic Information Systems (GIS) combined with 3D visualization technology or 3D virtual reality is an emerging tool for culture and architecture heritage applications [7]. That can be used to emulate the real world in three dimensions, with which users can participate in the virtual environment [8]. This tool handles spatial and attributes information of 2D as well as 3D objects through which 3D navigating and querying is possible. It has become an essential tool since its purpose is to extract the relevant information in the available data, thus helping it in Exploring, Analysing and Designing process. This combination between GIS and 3D virtual reality called Architectural photogrammetry Information Systems (APIS) [9]. Simply is abbreviated to Architectural Information Systems (AIS). 3D virtual heritage principle was achieved through APIS which represent the first phase of this research. Architectural photogrammetry Information Systems (APIS) was achieved to develop a three dimensional (3D) virtual model of the EL-Shenawy Palace and interactive visualization of the model as the research case study. At 1931 the palace got a certificate of the most beautiful

palace built outside of Italy signed by the Italians in the hands of Mussolini. At 2005 the Egyptian ministry of culture had bought the palace to convert it into museum of El-Mansoura heritage and now it is under restoration [1]. The palace is characterized by a unique character and a distinctive spatial configuration. However, the building today continues to degenerate; degraded due to lack of maintenance, and ill-conceived remodelling and additions inappropriate to its original character. The architectonical information system integrated to 3D models first of all allows an easy visualization and exploration of the model, at all programmed levels of detail; than the 3D model can be used as inquiring support for the information system: interrogating any element, at required scale, user can be informed, for example, about material, age of construction, documentation on maintenance or restoration, can visualize historical images and diagnostic reports: quickly, efficiently and in an interactive way the user can consult the whole documentation [10]. Architectural Information Systems is aimed to collect in a database, all the information, about materials, constructive technologies and conservation level of the monument. Alphanumeric data are supported by graphic data (pictures, drawings, maps) to describe state of historic building [11]. And to build spatial information system by using GIS for a historic building and to exemplify the features of the database for analysing and evaluating the data collected through a case study [5]. All data are entered into a relational database and within the GIS alphanumeric/textual and graphical/spatial information are linked to each other, creating a system able to manage process and analyse such a complexity of archaeological record in space and time [11]. APIS creates an integrated environment in which the spatially referenced data is connected with attribute data. The system provides the use of database system that enables making queries between different data. The visualization of these queries can be seen in 3D Model of EL-Shenawy palace that can be the utilization of multiple data queries. This related environment is a consequence of different data types produced by different software programs. Second phase of this research is to integrating the resulting data of Architectural photogrammetry Information Systems with Rough Neural Networks to build the prediction model. This integration called Geo-Information Neural System (GINS). The main purpose of (GINS) is to predict the remaining life of a historic building according to resulting data of first phase. It is based on a combined use of GIS ‘Geographic Information Systems’ and RNNs ‘Rough neural networks’ of the type: Multi-Layer Perceptron (MLP) with Feed-forward Back-error Propagation (BP) type of learning algorithm or




simply MLP-BP. Predicting is the process of making projections about future performance based on existing historical data. An accurate predicting aids in decision making and planning for the future forecasts empower people to modify current variables in the present to predict predict the future to result in a favorable scenario [12]. An An artificial neural networks differs from other forms of computer intelligence in that it is not rule- based, as in an expert system. An ANNs is trained to recognize and generalize the relationship between a set of inputs and outputs. In the case of artificial neural networks (ANNs), computing methodologies are being used to simulate how the human brain processes spatial data problems. The rough neural networks are special networks that are capable of dealing with rough boundaries of uncertainness through rough neurons. Rough Neural Networks (RNNs) are an upgrade of the ANNs. RNNs makes use of the rough set theory to deal with uncertainness levels. RNNs are built to care for vague boundaries of uncertainness through lower and upper neurons. The input rough neurons take the data as lower and upper bounds and process them through the network producing the corresponding output. This makes RNNs much better for geographical data which data is probabilistic and uncertain [13]. The geographic concrete data is preprocessed to eliminate an unwanted features (redundant features). This reduced data set is then feed into the RNNs which implement the Back-Propagation algorithm to adjust the connecting weights in the network. The input data are feed through weights which are updated through the training to turn the input into its lower and upper boundaries which input to the lower and upper neurons respectively. The hidden layer is also composed of lower and upper neurons connected to the input layer. The output layer is a single neuron which gives the input corresponding output. After training the RNNs are ready for testing new patterns of data so the test set is applied to the network to measure the network accuracy through measuring the overall absolute error of the test set. In an integrated system of GINS, simulation modeling and computer visualization, each technology contributes to the system with distinctive features. GIS provides the functions that allow a user to examine the spatial relationships among entities. Simulation modeling of Rough Neural Networks is capable of representing the dynamic relationship between cause and effect. The strength of visualization is to represent data in a way that may reveal patterns and relationship that are hard to detect using non-visual approaches such as text and tables [14].

By this way, the integration will achieve a good result and the value of geographical data will become more valuable. This proposed framework can be used to achieve a lot of tasks and extend the basic functionalities of traditional GIS to include modeling and simulation capabilities. The modern GIS will become more intelligent which will not only tell the user the result which based on specific search criteria but it will simulate to user what will happen in the future based on specific action or decision. The remainder of the paper is organized as follows: in section 2, Reviewed briefly some of the recent related work published in the area of integration between GIS, 3D virtual reality Technique and Rough Neural Networks. This leads to a discussion on integration between these techniques; in section 3, Explain Geo-Information Neural System Architecture at two phases. First phase, provides Architectural photogrammetry Information Systems (APIS) and its importance in architecture heritage preservation. Second phase, Build the prediction model by RNNs using the resulting data from first phase.Finally in section 4, Conclude with a short discussion.

2. Recent Related Work Published In the Area

Besides the introduction given above, a literary review is presented on integration between GIS, 3D Modeling and RNNs through their journey from inception to be implemented in various problems. Most of these studies are related to the documentation and characterization of historic buildings.

S. Günay [5], This project aims to analyze the transformation process of the data collected in conservation process into practical information in order to adapt this process to a spatial information system. In this context, use of Geographical Information Systems is tested in the process of historic building conservation on spatial information system designed for Do_anlar Church _zmir chosen as the case study.

M. Centofanti, R. Continenza, S. Brusaporci, I. Trizio [10]. The research group of L’Aquila University defined a procedure to create an architectonical Information System called SIArch- Univaq. This information system can be integrated with “Risk Map” Italian database. The SIArch-Univaq is based on importation of architectonical three-dimensional photorealistic models in GIS environment. 3D models are realised according to building constructive elements, derived by a critical architectonic surveying; the importation of models into GIS allows the interrogation of the constructive elements (i.e. beam, window, door, etc.): this favour the knowledge of the architectonical heritage, indispensable requirement to plan processes of restoration, maintenance and management.




NORBERT HAALA, Stuttgart [15]. Photogrammetric applications can benefit considerably from current advancements in the field of computer graphics. Within the paper this was demonstrated by an automatic texture mapping of building objects, which was realized using programmable graphics hardware. This development additionally results in the integration of virtual reality components like the photorealistic representation of landscapes within GIS. However, impressive visualization requires a large amount of geometric and graphical detail.

The Scientific and Technological Research Council of Turkey (TUBITAK), Project Number: 109Y174 [4]. This paper presents the most comprehensive study conducted so far for evaluating the corrosion levels related to air quality and the seasonal pollutant (NO2, SO2, and O3) exposure levels over 50 monitoring stations distributed on the historical peninsula of Istanbul. In the present study, seasonal exposure of NO2, SO2, and O3 pollutants were monitored using passive samplers, and corrosion attack values were calculated using dose response functions. The geostatistical analyst tool of ArcGIS® 9.1 was then used for generating GIS-based surface pollution and corrosion distribution maps.

Ahmed Abou El-Fetouh S, Mona Gamal [13], this research uses the RNNs in the bioinformatics field especially in the diagnoses problem. The medical concrete data is preprocessed using the weka data miner tool to eliminate any unwanted features (redundant features). This research tries to solve the diagnostic problems using the classification capabilities of the rough neural networks. The medical training data ,after preprocessing to remove unnecessary attributes, are applied to the rough neural network structure so as to update the connection weights iteratively and produce the final network that give a good accuracy rates.

Verónica Díaz*, Carlos López [16]. This paper presents a deterministic model for the damage function of carbon steel, expressed in ìm of corrosion penetration as a function of cumulated values of environmental variables. Instead of the traditional linear model, we designed an Artificial Neural Network (ANN) to fit the data. The ANN numerical model shows good results regarding goodness of fit and residual distributions.

Mohammed Hliyil Hafiz [17]. Predication Corrosion rate is quantitative method by which the effectiveness of corrosion control and prevention techniques can be evaluated and provides the feedback to enable corrosion control and prevention methods to be optimized. In this paper, Novel Model to predict corrosion rate based on RBFNN was proposed. A model is produced from experimental work for one year and eighty four specimens were used through this work using anode with a high level of precision.

3. GEO-INFORMATION NEURAL SYSTEM (GINS)

The main objective of this study is to preserve Architectonic cultural heritage buildings from surrounding environment threats. So this study apply three new technologies GIS, 3D Virtual Reality and Rough Neural Networks to achieve comprehensive study and analysis of architectonic cultural heritage environment at two stages. First stage, Architectural photogrammetry Information Systems represent instrument of two way-process between 3D documentation of heritage building and geographic analysis of its environment. Second phase, build prediction model using Rough Neural Networks by model the environmental factors that influence corrosion of heritage building and derive relationships between them and the rate of the resulting corrosion.

3.1 Architectural Information System

The rapid development of information technology at the end of the last century has led to the realization [18]. The demand for 3D spatial data and 3D modeling is changing traditional Geometrics Engineering applications from 2D to 3D representations. The aim of this study is to build on the new concept of Architectural photogrammetry Information Systems (APIS) and to investigate the integration of 3D photogrammetry into a 3D GIS environment. Through using the information instrument to collect, analysis and easily questioning the large quantity of data related to historical architectonical heritage. Then it’s defined a database referred to the spatial dimension of architecture that can integrate alphanumerical, raster and vectorial information. To reach this aim its necessary define a geo-referenced information system integrated to digital three-dimensional architectonical models.

3D Digital Photogrammetry has played a central role in this regard. The first is to discover the past. The second proposal is to protect the architectonic cultural heritage. It represents an important source of historical collective memory and also an instrument for historical and scientific studying [11]. The third proposal is to identify the threats to cultural heritage. Alphanumeric data are supported by graphic data (pictures, drawings, maps) to describe conservation state of monument. Using 3D Photogrammetry model for heritage buildings are significantly important both obtaining sufficient information about the buildings and better visualization of them. Especially a realistic model determined by texturing will be helpful for the users to better understand the structures. 3D Photogrammetry model are best suited to give a clear and detailed impression of existing situations.




APIS have two kinds of databases: the one who has the graphical and geometrical data and the other who has the alphanumeric data related to the graphical data. The first one will be generated from the photogrammetric digital system or from other graphical external files (like CAD plans, photo texture etc.). The second one will be handled with the DBMS ACCESS [19]. Both databases will be linked and managed together by the GIS software Arc Scene 10.1, creating the information system. Which play a vital role in analysis architectural heritage buildings environment.

The use of GIS-systems in this stage shifts from 2D to 3D modeling and analysis. APIS is based on importation of architectonical three-dimensional photorealistic models in GIS environment. 3D models are realized according to building constructive elements, derived by a critical architectonic surveying. The main components of 3D GIS Environment are: 3D Visualization and 3D Modeling and Data Management. A 3D GIS system should be scalable with multiple representations so that a user can really interact with the 3D Objects. 3D Visualization is success keys to plan and design the future. The presentation of 3D GIS should be focused on reality-based geospatial objects [7]. This favour the knowledge of the architectonical heritage, indispensable requirement to plan processes of restoration, maintenance and management.

The Architectural photogrammetry Information Systems (APIS) prepared for EL-Shenawy Palace can be classified into four main phases. The first one is the data acquisition phase, which covers the metric survey of the building and data collection about various aspects. The second phase is the generation of a 3D virtual model by 3D object reconstruction and photorealistic texture rendering, and interactive visualization of the virtual model on a computer. The third phase is the “data structuring”. In this phase, the collected data is transferred to the GIS by designing a database (graphical / spatial data) and defining a data model. Then comes analysis and evaluations where queries are made between different data topics [5]. Following these phases, all the raw and processes data are visualized in the 3D GIS environment. This system also enables users to query, analyze, share access, analyze or transform the spatial data. Using available software such as Autodesk AutoCAD 2010, Autodesk 3ds Max 2012, ArcGIS/ArcScene10.1, and Adobe Photoshop CC is used to build 3D GIS environment. After construction of 3D modelling of heritage building a GIS based database will be designed. In this database, there will be 3D model, historical information and data of the historic building together with the other data related to building collected during the study [20]. As mentioned above, integration of 3D photogrammetry and GIS leads to the efficient use of data, analysis and

presentation opportunities, which are very important for saving the architectonic cultural heritage.

Figure 1: Architectural Photogrammetry Information

Systems Phases

Figure 2: EL-Shenawy Palace Italian Certificate as the

most beautiful palace built outside of Italy

Figure 3: Autodesk 3D Max Screenshots of El-Shenawy

Palace Exterior and Interior

Figure 4: Screenshots of EL-Shenawy Palace in Arc

Scene 3.2 Rough Neural Networks




Architectural heritage buildings are considered alive objects affect and are affected by the surrounding environment as anything on earth. Exposed to different geographic factors and threats that reduce its life and threaten to eliminate them and disappearance forever. So it became necessary to change the traditional ways and methods to the study and analysis of these geographical factors and their impact on heritage buildings. Artificial Neural Networks are a complex system may be decomposed into simpler elements, in order to be able to understand it. Also simple elements may be gathered to produce a complex system. Networks are one approach for achieving this. There are a large number of different types of networks, but they all are characterized by the following components: a set of nodes, and connections between nodes. The nodes can be seen as computational units. They receive inputs, and process them to obtain an output. This processing might be very simple (such as summing the inputs), or quite complex (a node might contain another network...) the connections determine the information flow between nodes [21].

In this paper, The RNNs used to predicate corrosion rate depending on resulting data from APIS. Learning data was performed by using a 36 samples test with different Environment Resistivity (ER), Impressed Current (IC), Location of Anode (LA), Corrosion Current (CC) and Corrosion Rate (CR). The RNNs model has five input nodes representing the (ER, IC, CC, LN, and SA), sixteen nodes at hidden layer and one output node representing corrosion rate (CR). Simulation test use 6 data samples taken from the experimental results to check the performance of the rough neural networks on these data and shows the proposed model can be used successfully to predicate the corrosion rate [17]. This can be done by choosing the best neural network architecture, decision function and learning algorithm for this application. Air Corrosion refers to the disintegration of materials into their constituent atoms because of chemical or electrochemical reactions with the surrounding environment. This phenomenon is experienced in day to day living. This disintegration causes a loss in the thickness of the construction which results in a decrease in resistance and strength and consequently a decrease in the service performance of the construction [16]. Atmospheric Corrosion can result in the destruction of materials in a gradual manner and hence shortening their lifespan. Forth where environmental factors affect the material in complicated processes leading to its corrosion. The most common examples of corrosion include rusting, discoloration and tarnishing [22]. An understanding of air corrosion and an ability to predict corrosion rate of a material in a particular environment plays a vital role in

evaluating the residual life of the material. In order to understand and predict corrosion, first must model the environmental factors that influence corrosion and derive relationships between them and the rate of the resulting corrosion [16]. This model takes into account the present multi-pollutant situation of the most critical pollutants causing corrosion on the architectural [4]. In the scope of this paper, the testing data obtained from the Egyptian Environmental Affairs Agency, East Delta Sector-Environmental Measurements Laboratory are evaluated. They are emitted by industrial activities and by the transport sector. These pollutants create problems on the local scale, but they are also transported in the air over long distances [3]. An increased level of pollutant concentrations makes corrosion progress considerably faster, possibly exponentially, and decreases the life of architectonic cultural heritage. The present study was aimed at analysing the potential effects of air pollution on the corrosion of materials of buildings, with a major focus on architectonic cultural heritage buildings.

Figure 5: Interaction of atmospheric pollutants,

meteorological conditions and deposition mechanisms in the process of atmospheric corrosion.

Figure 6: Rough Neural Networks Structure

Rough neural networks are like conventional neural networks in their training algorithms and connection mode, but they differ in the neuron used in the network. Instead of the conventional neuron the rough neural use a pair of neurons to represent the rough neuron. One for the upper approximation and the other for the lower approximation of the feature or attribute that the neuron represents. A back propagation network in the rough mode is composed of three layers. The input layer is composed of the features used in the model which the rough neural network used to simulate. Each feature is represented by two neurons connected imaginary with each other to facilitate information exchange. The input




feed comes to the lower and upper neurons from the external world and first multiplied by a weight connection connected to the neurons. This weight connection is updated during the learning phase along with other weighted connections in the network. The hidden layer is composed of a number of rough neurons calculated by the Baum-Haussler rule [13]:

Where Nhn is the number of hidden neurons, Nts is the number of training samples, Te is the tolerance error, NI is the number of inputs ( attributes or features), and No is the number of the output.

Each neuron in the hidden layer is actually represented by two neurons which take its input feed from the input layer. This can be expressed as if each of the input and hidden layers contains two sub layers one for lower approximation and the other for upper approximation. The lower approximation neurons in the input layer are fully connected to the lower approximation neurons in the hidden layer and in the same way the upper approximation neurons are connected. The output layer is composed of one conventional neuron to produce the output of the network. The connections between the hidden layer and output layer is a full connection mode but the lower and upper neurons in the hidden layer are treated as if they were rough neurons producing only one output and one connection weight for each rough neuron

The output of a rough neuron is a pair of upper and lower bounds, while the output of a conventional neuron is a single value. Let (ILn,OLn) is the input/output of the lower rough neuron and (IUn,OUn) is the input/output of the upper rough neuron. The calculation of the input/output of the lower/upper rough neuron is given by the following equations:

The training of the rough back propagation neural network is just like the conventional one but the equations in section are used to calculate the output of lower and upper neurons in both the input and the hidden layer. The conventional neuron in the output layer is calculated by the transfer function. The transfer function used in this model is the sigmoid function illustrated by the equation.

Where x is the neuron input and λ is a constant which its value is chosen according to experiments. The output of the rough neuron (O) will be computed using the following equation:

The previous equations are used to calculate the output from the hidden neurons layer to the output neuron. Only equations 2, 3, 4 and 5 are used to calculate the lower and upper neurons output from input layer to hidden layer.

The training data set is further divided to train the network on different training data set sizes and measure the accuracy for both the neural network and the rough neural network. In the present study, inputs are selected as five inputs these are (Environment Resistivity (ER), Impressed Current (IC), Location of Anode (LA), Surface Area (SA), and Corrosion Current (CC)) and it have one output which is the predicted Corrosion Rate (CR) [17].

Where: iccorr= corrosion current density, μA/cm2 ΔW = weight loss, g n = number of electrons F = Faraday’s number= 96487 C/g eq. Mt = molecular weight of material (Fe= 55.847 g/mol) t = exposure time, s A = surface area of material specimen, cm2

The experiment results were used to train rough neural network which have been constructed and trained using 36 data samples from the experimental data and six samples were used for generalization test of the trained rough neural network as shown in table 1. The experiments proved that the rough neural network is better than the neural network. First, data set converted to normalized data set .Then used in the model and the experimental results along with a comparison. With neural networks working on the same data set is declared. This module measures the network accuracy rate by applying the testing data to the network and comparing the network output with the actual output. The accuracy rate is computed in terms of the complement of the error rate resulting from the network. The error rate is computed by the absolute error rate equation.

The rough neural network structure, the back propagation training algorithm and the testing are implemented in C# code. After a number of trials to reach the best accuracy, the learning rate initial value and the λ are found to be 0.5 and 6 respectively.

(2)

(6)

(1)

(7)

(4)

(5)

(8)

(3)

(4)

(5)

(6)

(9)




Table1: Sample of experimental data Resistivity Ohm.cm

Impressed current density, μA/cm2

Location of anode from specimen, cm

Corrosion current, μA/cm2

Corrosion rate, gmd

0

0.85

0 0.182

0.207 0.529

0

0.547

0.56

0.89

0.333

0.182

0.207

0.569

0.333

0.635

0.647

0.908

0.666

0.272

0.296

0.587

0.666

0.545

0.557

0.919

1

0.182

0.207

0.597

1

0.547

0.577

0.102968

0.679

0

0.0906

0.12

0.357

0

0.362

0.382

0.723

0.333

0.275

0.118

0.399

0.333

0.272

0.294

0.751

0.666

0.914

0.12

0.429

0.666

0.274

0.295

1

1

0.182

0.207

0.457

1

0.366

0.371

1

0.536

0

0

0.0314

0.268

0

0.182

0.385

0.6

0.333

0.089

0.12

0.332

0.333

0.182

0.207

0.688

0.666

0.0907

0.118

0.399

0.666

0.272

0.295

0.729

1

0

0

0.462

1

0.182

0.471

Table2: Comparison between ANNs, RNNs Results

Resistivity Ohm.cm

Impressed current density, μA/cm2

Location of anode from specimen, cm

Corrosion current, μA/cm2

ANNs Model Corrosion rate, gmd

RNNs Model Corrosion rate, gmd

0

0.85

0

0.207

0.091 0.415

0.569

0.333

0.647

0.297

0.692

0.102968

0.751

0.666

0.12

0.353

0.625

0.457

1

0.371

0.364

0.38

1

0

0

0.385

0.383

0.461

0.191

1

0.471

0.371

0.482




Figure 7: Error rate comparison for different training set size

Rough Boundaries are a very good way to handle uncertainty in geographic data. The rough neural networks are simulations to human thinking in terms of lower and upper boundaries of the rough set. The experiment results are shown and proved that the proposed model of rough neural network is better and efficient than ordinary neural networks. 4. CONCLUSION Architectural Cultural heritage protection is an important issue in the world as constitute an important part of past. The Architectural cultural heritage in real environment are irreversibly damaged by environmental disaster or atmospheric damages. Those damages sometime were discovered too late. During the last decades, Architectural cultural heritage been facing serious environmental and urbanization problems. As a result, most of the architectural cultural heritage are in danger of deterioration, destruction, corrosion, and soiling. In this regard, reliable and up-to-date data on air quality and its effects on heritage stock are very important components of the environmental impact assessment, cost benefit analysis, and risk management for cultural heritage objects. One of the major conclusions of this study is that the conservation process of historic building requires the management of collected and evaluated data in order to improve the quality of information gathered. The data is the major component of conservation decision making process and information systems are efficient tools for storing, organizing, analyzing, evaluating and monitoring this data. So it is important join different items like Architectural Information System and Rough Neural Networks to create comprehensive study including all historical information and data of the historic building together with the other data related to historic building environment. The present study has indicated that the Architectural Cultural heritage Buildings in Egypt is under the risk of corrosion attack. The location of the heritage Buildings has been exposed to not only significant levels of air

pollution but also a certain level of humidity and temperature. Air pollution exposure studies and corrosion estimations should be further continued to measure the gains. REFERENCES [1] Yaldiz Y. Eid, SUSTAINABILITY OF 19TH AND

20TH CENTURY BUILDINGS IN EGYPT, ERJ SHOUBRA FACULTY OF ENG, Number 6 January 2006.

[2] Michel Chapuis, Adèle Lydon and Astrid Brandt-Grau Directorate-General, Preserving our heritage, improving our environment, Volume II Cultural heritage researchFP5, FP6 and related projects, 2009.

[3] Stefan Doytchinov, Augusto Screpanti and Giovanni Leggeri ,Effects of Air Pollution on Materials, Including Historic and Cultural Heritage Monuments, EAI Speciale II-2012 Knowledge, Diagnostics and Preservation of Cultural Heritage, 2012.

[4] Ferhat Karaca, Mapping the corrosion impact of air pollution on the historical peninsula of Istanbul, Volume 14, Issue 2, March–April 2013.

[5] S. Günay, SPATIAL INFORMATION SYSTEM FOR CONSERVATION OF HISTORIC BUILDINGS CASE STUDY: DOGANLAR CHURCH Izmir, vol. XXXVI/5-C53, October 2012.

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