Prioritizing Projects Compare and contrast the two articles attached to this question and create a three- to four-page Word document (synopsis) i

RESEARCH ARTICLE

Expert system for selecting and prioritizing projects for handling urban water supply crises Welitom Ttatom Pereira da Silvaa and Marco Antonio Almeida de Souzab

aDepartment of Sanitary and Environmental Engineering, Federal University of Mato Grosso, Cuiabá, Brazil; bDepartment of Civil and Environmental Engineering, University of Brasília, Brasília, Brazil

ABSTRACT The water supply crisis (UWC) has affected various cities around the world. The variability of possible causes, the many viable alternatives to UWC management and methodologies for selecting these alternatives, as well as local government’s economic and technical constraints make the problem complex. The aim of this paper is to help select a set of alternative solutions suitable for the UWC problem. The proposed methodology comprised the following steps: (1) theoretical foundation, (2) planning the expert system (ES) to be built, (3) formal knowledge explicitation, (4) knowledge coding, (5) evaluation and adequacy of ES and (6) application of ES to real-life UWC cases. The main result was a computational decision support system, called UWC-ES. The conclusion was that UWC-ES behaved as a computational tool that reasonably reproduces knowledge from various human experts with accepta- ble applicability, and considering the possibility of using it in other cases.

ARTICLE HISTORY Received 24 January 2018 Accepted 24 September 2018

KEYWORDS Management strategies; rule-based expert system; water crisis

1. Introduction

The urban water supply crisis (UWC) is currently a significant problem affecting many populations around the world. Numerous UWC cases can be found in the literature, such as the city of São Paulo (Brazil), the provinces of northern and western China, California (USA), the city of Cape Town (South Africa), and the western prairie provinces of Canada, which have been described in Coutinho, Kraenkel, and Prado (2015); Zheng et al. (2010); Pollak (2010); Ziervogel, Shale, and Du (2010), and Schindler and Donahue (2006), respec- tively. This specific problem has motivated researchers to seek alternative solutions and methodologies to cope with them adequately.

The alternative solutions are varied and may consider struc- tural strategies (technological options to reduce water con- sumption, such as using water-saving equipment), non- structural (actions that influence demand, such as changes to pricing policies) and the combination of structural and non- structural strategies. A more detailed discussion of structural and non-structural strategies is presented in Savenije and Van der Zaag (2002). The analysis methodologies for handling the UWC include traditional optimization methods, simulation and scenario generation techniques, statistical models, multiobjec- tive and multicriteria methods, among others. For example, Zarghami, Abrishamchi, and Ardakanian (2008) carried out studies aiming to select alternative water management mea- sures in an environment with significant population growth and frequent water supply failures (in the case of the city of Zahedan, Iran). A multiobjective and multicriteria model for the problem of water supply contemplating several variables (losses in the water network, consumption measures and others) was developed. Different criteria (costs, need for

water supply, etc.) were aggregated using the Compromise Programming method. The results showed that demand man- agement measures can delay water transfer projects to the city of Zahedan for more than 10 years. Artificial intelligence techniques have also been used (León et al. 2000; Tillman et al. 2005; López-Paredes, Saurí, and Galán 2005).

To analyze this context where there are various alterna- tive solutions and different methodologies, faced with situa- tions of severe limitations of financial and human resources that many Brazilian cities and cities throughout the world commonly go through, the following question arises: how to select alternative solutions for a given UWC problem? As a response, using UWC classification techniques and con- structing an expert system is suggested based on studies by Silva and Souza (2017) and Liao (2005). Thus, this study aims to help select suitable project alternatives for the UWC problem. More specifically, it is hoped that a system can be obtained to support the decision-making process of selecting priority projects to solve the UWC problem in urban environments with a significant limitation of financial and human resources.

2. Methods

The proposed methodology comprised the following steps: (1) understanding the real problem using a literature review and a study of the theoretical foundations of possible solutions, (2) planning the expert system to be developed, (3) formal knowl- edge explicitation, (4) knowledge coding and development of the expert system (ES), (5) evaluation and adequacy of the ES and (6) application of the ES to real-life UWC cases to verify the acceptability of the developed ES response.

CONTACT Welitom Ttatom Pereira da Silva [email protected]

URBAN WATER JOURNAL 2018, VOL. 15, NO. 6, 561–567 https://doi.org/10.1080/1573062X.2018.1529806

© 2018 Informa UK Limited, trading as Taylor & Francis Group

2.1 Theoretical foundation

In this section, topics such as UWC classification (typification) and the expert system (ES) are presented, which are the basis for this study.

Using typical cases (case classification) plays an important role in decision-making, especially when the decision involves a large number of indicators and/or influencing factors (López- Paredes, Saurí, and Galán 2005; UNEP/UNESCO 1987). In this study, the decision support model for crisis management in urban water supply (UWC-MODEL), developed by Silva and Souza (2017), was used to simplify the analysis and study different UWC situations (classification of UWC cases).

The UWC-MODEL performs the following activities: (1) it aggregates influential factors in the UWC into five levels (socioeconomic, management, environmental, urban and cul- tural), (2) it evaluates its intensity of contribution to the UWC situation for each level and (3) based on this evaluation, it classifies the UWC situation at each level (classes: very strong, strong, moderate, weak and very weak). The UWC-MODEL can classify and/or typify UWC cases and, consequently, the cause of the UWC is identified, helping to select and prioritize pro- jects handling UWC. For example, in a case that has a very strong contribution from the cultural level, the measures to restructure the urban water supply system should prioritize projects related to the cultural level. Therefore, setting up an environmental education program could be an appropriate project for handling UWC. In Equations (1) and (2), results from the UWC-MODEL (RUWC-MODEL), the basis and starting point of this study, are presented in vector format, where Cj=1, Cj=2, Cj=3, Cj=4 and Cj=5 are the classes of socioeconomic, management, environmental, urban and cultural levels, respectively. More details about the UWC-MODEL can be found in the study by Silva and Souza (2017).

RUWC�MODEL ¼ Cj¼1; Cj¼2; Cj¼3; Cj¼4; Cj¼5 � �

(1)

RUWC�MODEL ¼ fo; mfr; fr; mo; mfof g (2) Another basis for this research was using the technique to generate expert systems (ES). Artero (2009) defined ES as a computational system designed to represent the knowledge of one or more human experts on a particular domain and, from the processing of the knowledge base, seek solutions to problems that, in general, require a great deal of specialized knowledge.

In an ES operation, it is assumed that the user feeds the ES with factors or information and the system provides the user with expert knowledge. Internally, ES consists of two main components: the knowledge base and inference engineering. The knowledge base stores knowledge and inference engi- neering uses stored knowledge to construct the conclusions. Some basic concepts refer to the problem domain, the domain knowledge and the inference engineering. A problem domain refers to a problem specific to an area (medicine, finance, science or engineering) that the expert can solve. The expert’s knowledge of how to solve a specific problem is called domain knowledge. Inference engineering refers to the ability the ES has to infer in the same way a human expert should infer when faced with a problem.

The general strategies for ES development are shown by Giarratano and Riley (2004). Briefly, the ES development pro- cess consists of: (1) the ES developer establishes a dialogue with the experts for the expert knowledge explicitation, (2) the developer encodes the explicit knowledge (ES development), (3) the experts evaluate and criticize the developed ES, the developer makes adjustments and the process is repeated until the ES is considered adequate by the experts. In practice, the ES is an executable program that searches for the knowl- edge about its domain in a separate file. This means that the knowledge base can be completely changed and even then, the program will work normally, adopting the knowledge from the new base (Artero 2009). Some suggested references on the subject are: Kim, Wiggins, and Wright (1990); Wright et al. (1993); Nikolopoulos (1997); Resende et al. (2005); Artero (2009); Giarratano and Riley (2004) and Liao (2005).

2.2 Expert system planning

The purpose of the ES planning stage was to produce a formal plan for ES development called the UWC-ES. Thus, the feasi- bility assessment, resource management and preliminary func- tional layout tasks were performed based on recommendations made by Giarratano and Riley (2004). For the feasibility assessment task, the factors and returns sug- gested by Giarratano and Riley (2004) were verified, in order to decide if the ES approach would be adequate. The resource management task was carried out by researching the compu- ter resources (software and hardware), human resources and financial resources to develop the UWC-ES. In order to do this, a literature review of the resources used to develop precursor ESs with similar objectives was carried out, and a comparison was made with the resources available to develop the UWC- ES. The preliminary functional layout task should define what the system will achieve by specifying the system functions. Thus, the objectives of the ES were carefully analyzed in order to define the functions of the system, following recommenda- tions by Giarratano and Riley (2004).

2.3 Formal knowledge explicitation

Knowledge explicitation refers to the process of acquiring the knowledge needed to solve the problem (domain knowledge). To do this, the activities used by Collier, Leech, and Clark (1999); Tillman et al. (2005) and Patlitzianas, Pappa, and Psarras (2008) were adapted. In this case, these activities included: (1) defining the population universe of simulated UWC cases, (2) defining the sample analyzed by the experts, (3) identifying projects for handling UWC and (4) obtaining domain knowledge. A total of 13 specialists (five with a mas- ter’s degree and seven with a doctorate degree) were consid- ered, of which six were working in the sanitation area, two in the environment area and five in the water resources area, six linked to water regulatory agencies, two to the environmental protection agency and five to research institutions and universities.

The population universe of simulated UWC cases is the total possible number of combinations of the UWC-MODEL classifications. Thus, 3125 (five levels and five classifications,

562 W. T. P. D. SILVA AND M. A. A. D. SOUZA

N = 55) individuals or typologies of simulated UWC cases were observed that form the population universe. To define the sample to be analyzed by the experts, the simple random sample method was used. As justification, this method of sampling leads to the sample in which each typology of the sample population has the same probability of being selected, not privileging specific situations or cases. The number of sample units (n) was defined in 10% of the population, which made a total of 313 typologies analyzed by the experts. To identify the projects for handling UWC, a literature review was carried out. Identifying priority projects (PP) for handling UWC by experts for the ‘n’ sample units yielded the training database, an initial part of the task of obtaining the domain knowledge. For this purpose, the UWC (UWC-MODEL) classifi- cation and/or typology information, the identification of pro- jects for handling UWC, the sampling technique used and the samples to be analyzed were made available to the experts. The experts were then asked to identify PP for handling UWC (selection of five major projects for handling UWC) for each of the typologies of the real-world/simulated cases analyzed by them. For exemplification, from the process of obtaining the training database, a graphical representation is illustrated in Figure 1.

Having defined the training database, the final part of obtain- ing the knowledge domain (obtaining the rules) was started. Moreover, a machine learning technique was used for this pur- pose, which automatically extracts information from the training database. More specifically, a decision tree was used as the classification model, which is one of the most widely used machine supervised learning methods in practice (Artero 2009). The method is based on the decision tree construction, from the training database to obtaining the production rules (domain knowledge). For the construction of the decision tree, algorithm J48, which is one of the most known and used algorithms for constructing decision trees, was used (Artero 2009). To evaluate the classification model (decision tree), the Confusion Matrix and Kappa Statistics (κ) were used, as recommended by Resende et al. (2005). Furthermore, it was considered that the classifica- tion model would be adequate if it presented Kappa Statistics (κ) values equal or above κ = 0.41 (moderate agreement), according to Landis and Koch (1977). Otherwise, adjustments in the classi- fication model would be necessary.

2.4 Knowledge coding

For knowledge coding, a Pentium 2.13GHz microcomputer was used, with 4GB of RAM in the Windows operating system

using CLIPS (C Language Integrated Production System) shell, version 6.3. In this case, it was adopted as a robust and efficient shell for ES development, one that: (1) presented the ability to resolve conflicts between rules, (2) operated satisfactorily with the forward chain, (3) was a free access shell and (4) presented good answers (accuracy). This robust shell definition considered the existence of conflicting opi- nions among the experts consulted, the proposition of the Modus Ponens type ‘if (condition) – then (action)’ as an appro- priate form of inference, and the economic limitation for commercial shell acquisition. Thus, the CLIPS shell can be evaluated as robust to the problem in focus agreeing with the works of Riley et al. (1987); Mettrey (1991), and Kuesten and McLellan (1994).

2.5 Evaluation and adequacy

According to Giarratano and Riley (2004), at this stage, the expert should evaluate and criticize the UWC-ES, passing on this information to the ES developer, who in turn performs the adjustments and again returns the ES to the expert for re- evaluation. This process is iterative until the expert judges that UWC-ES is adequate. Considering the characteristics of the problem and the studies carried out by Spring (1997) and Collier, Leech, and Clark (1999), the Turing test (a classic test that aims to verify if a machine has the intelligence matching that of a human). To implement the test, the methodologies used by Spring (1997); Collier, Leech, and Clark (1999), and Artero (2009) were adjusted.

The Turing test is based on forming three groups of differ- ent experts, indicated here by G-1, G-2 and G-3. The test basically consists of collecting a set of ‘m’ test cases, previously solved by experts from the G-1 group, solving these cases by developed ES (G-2), carrying out the specific evaluation of both solutions, S (G-1) and S (G-2) by other experts (G-3). In the specific evaluation, two outputs were requested from the G-3 group; the first output refers to the quality evaluation of the G-1 and G-2 solutions, according to a scale ranging from 1 to 7 (1 = very bad, 4 = reasonable, 7 = very good). In the second output, the identification of the solutions from the ES was requested. If G-3 assigns a value greater than or equal to 4 to the quality of solutions presented by G-2 and cannot deter- mine (with a minimum of 50% accuracy) which one of the two (G-1 or G-2) is the group of experts, it is said that the machine has passed the Turing test and therefore can simulate human intelligence. In this case, the end of the UWC-ES development is observed, and the ES is considered suitable to select the

Figure 1. Obtaining the knowledge domain.

URBAN WATER JOURNAL 563

best solutions for the UWC problem. Otherwise, adjustments must be made in the UWC-ES.

2.6 Application of the expert system

The purpose of the application cases was to help evaluate the results of the developed ES model. Considering the prospect of possible water supply problems in the Federal District, as mentioned by Conejo et al. (2009), some of the Administrative Regions (AR) of the Federal District were adopted as case studies. These AR included Brasília, Lago Norte, Cruzeiro, Guará, Varjão, Estrutural and Park Way. These AR were chosen according to the importance of studying urban environments with different economic levels. For ES application, secondary information was used, based on data from Silva (2012). In addition, the Federal District Government was considered as the decision-maker in the case, with its respective competent institutions (Brazilian Federal District’s Regulatory Agency for Water, Energy and Sanitation – ADASA, Brazilian Federal District’s Water Supplier and Sanitation Company – CAESB, Brazilian Institute of Environment and Water Resources – IBRAM and Secretary of State for the Environment – SEMA).

3. Results

Based on following the formal plan and setting the predefined tasks for UWC-ES development (expert system planning stage), responses about its viability were obtained. The result of the feasibility assessment task, the verification of the factors and returns suggested by Giarratano and Riley (2004), led to the return of the viability response of the ES approach. The rea- sons that led to this response refer to the fact that most of the returns (factors 1, 3, 4, 5 and 6) showed a favorable return to ES development, as shown in Table 1.

For the resource management task, the result indicated that the available resources are comparable to the resources used to develop other ES with equivalent functions, according to the literature review (Cheng, Yang, and Chan 2003; Chau, Chuntian, and Li 2002; León et al. 2000). Based on the pre- liminary functional layout task, it was found that the proposed ES must ensure compliance of the purpose of pointing out priority projects for handling UWC. From the knowledge expli- citness stage, the population universe (possible combinations, which make a total of N = 3125) and the identification (Id.) of the sample units (typologies, totalizing n = 313) were identi- fied to be studied.

As a result of the task of identifying projects for handling UWC, Table 2 shows a summary list obtained from a literature review.

As the problem was modeled to obtain five priority projects (PP) for handling UWC from the experts, five classification models (decision trees) were found, one for each priority estimate (PP1, PP2, …, PP5). Part of the classification model (decision tree) and respective production rules (domain knowl- edge) obtained for PP1 are presented in Figure 3(a,b).

In total, 409 production rules were obtained that make up the domain knowledge. Additional information on these clas- sification models (decision tree) and production rules was presented in Silva (2012).

As a result of the evaluation of the classification model (decision tree), the Confusion Matrix and the Kappa Statistics (κ) were obtained. The Confusion Matrix is shown in Figure 2(c). The Confusion Matrix provides an effective measure of fit for the classification model by showing the number of correct classifications versus the number of classifications predicted for each class, concerning a training database. Thus, the correct classification of the model (coincidence of the response pre- sented by the expert, shown in the lines, and the response presented by the classification model, presented in the col- umns) is given by the diagonal elements of the Confusion Matrix. The total number of training data correctly classified by the classification model for PP1 is given by the sum of the elements in the diagonal of the Confusion Matrix, and all others were incorrectly classified. Therefore, a reasonable fit of the classification model (decision tree) of PP1 was observed in Figure 2(c). Moreover, it should be mentioned that the other classification models presented slightly better results.

For the average Kappa Statistics (κ), whose individual values for each classification model (decision tree) are κPP1 = 0.41, κPP2 = 0.49, κPP3 = 0.54, κPP4 = 0.49 and κPP5 = 0.45, an average value of κ = 0.48 was found, consid- ered adequate according to the adopted methodology. This value indicates that the classification showed a moderate agreement. The classification model presented a moderate adjustment and, according to Landis and Koch (1977), can represent, with moderate precision, the training data.

Table 1. Factors and returns considered in the ES viability assessment.

Item Factora Returnb Evaluationc

1 Can the problem be solved efficiently by conventional programming?

No No

2 Is the problem’s domain well defined? Yes No 3 Is there a need and interest for an ES? Yes Yes 4 Are there human experts willing to cooperate? Yes Yes 5 Can the experts pass on their knowledge? Yes Yes 6 Does the solution of the problem mainly involve

heuristics and uncertainty? Yes Yes

Notes: a) Factors suggested by Giarratano and Riley (2004), b) expected return for the ES approach to be viable, c) return found after feasibility assessment.

Table 2. Summary list of projects for handling UWC.

P Projects for handling UWC

P1 Loss reduction (S) P2 Macro and micro-mediation implementation (S) P3 Implementation of individualized measurement (S) P4 Implementation of efficient bathrooms (S) P5 Reduction in pressure in the hydraulic system in bathrooms (S) P6 Reduction in pressure in the water distribution network (S) P7 Rainwater collection and use (S) P8 Greywater collection, treatment and use (S) P9 Setting up environmental education programs (NS) P10 Application of fiscal stimuli for consumption reduction (NS) P11 Tax on inefficiency in water use (NS) P12 Adjustment of tariff policy (NS) P13 Regulation of the water consumption of household appliances/savers (NS) P14 Increase in production capacity (S) P15 Intermittence/rationing in the supply system (S) P16 Regulation of consumption (NS) P17 Creating green roofs (S) P18 Strengthening water supply operator (NS) P19 Using good practices for water conservation (NS) P20 Privatization/concession of the water supply services operator (NS)

Note: (S) is structural measures and (NS) is non-structural measures.

564 W. T. P. D. SILVA AND M. A. A. D. SOUZA

The knowledge coding step occurred satisfactorily. The tool used was considered adequate as the production rules and conflict resolution strategies were easy to implement. Figure 3 shows the CLIPS development environment and part of the elaborated coding.

The results of the UWC-ES evaluation and adequacy stage indicated that the first group, the G-1 group, was formed by the 13 experts who effectively contributed to forming the training database (domain knowledge). The second group (G-2) was formed by the answers given by the ES, i.e. it refers to the UWC-ES. Furthermore, the third group was the G-3, formed by three experts who did not participate in obtaining domain knowledge. The first output, given by the G-3, indi- cated an average value of 4 for the quality of the solutions presented by UWC-ES, on a scale ranging from 1 to 7 (1 = very bad, 4 = reasonable, 7 = very good). When analyzing the quality of the solutions presented by the G-1 human experts, which was also 4, a similarity can be observed between G-1 and G-2. This also shows a reasonable divergence between the opinions of the human experts of the G-1 group and the G-3 group. These divergences are also conveyed in the responses given by the ES. It was observed that cases with similar characteristics receive different solutions, depending predomi- nantly on the training, experience and professional experience of the expert who analysed the case. This fact requires careful use of the results of the developed ES (UWC-ES) and proves the complexity of the studied problem. Similar problems were

reported by Giarratano and Riley (2004) because even among the experts there is no consensus.

The second result indicated that the G-3 was unable to determine, with 67% accuracy, which of the two (G-1 or G-2), is the group of human specialists, therefore UWC-ES was approved by the Turing test. In other words, it can be con- cluded that the UWC-ES is able to select the best solutions to the problem of handling UWC.

The main results found for the case studies chosen, after using the UWC-MODEL, are presented in Table 3. These were the results used to feed the UWC-ES.

According to the UWC-MODEL, the environmental level (j = 4) was the one that presented the greatest contribution to the intensification of the studied UWC. For the second and third level of greatest contribution, the urban dimension (j = 3) and managerial dimension (j = 2) were found, respectively. This suggests that the PPs selected by the UWC-ES for solving the UWC case studies are targeted at reducing the contribu- tion or collaboration, of the environmental, urban and man- agerial levels. The results obtained for the case studies, after using the ES (input of the results of the UWC-MODEL in the UWC-ES) are presented in Table 3.

In summary, eight PPs were suggested for the solution of the studied case of UWC, which are the following: loss reduction (P1), implementation of individualized measurement (P3), rainwater collection and use (P7), greywater collection, treatment and use (P8), application of fiscal stimulus for consumption reduction

Figure 2. (a) Part of the classification model (decision trees); (b) production rules; (c) confusion matrix of PP1.

URBAN WATER JOURNAL 565

(P10), consumption regulation (P16), strengthening water supply operator (P18) and use of good practices for water conservation (P19). When analyzing the results presented by the UWC-ES, some problems can be observed, such as the recommendation of the guideline ‘implementation of individualized measurement’ (P3) for the Estrutural and Varjão AR, whose predominant housing typol- ogy is isolated single-family residences that already have indivi- dualized measurement; the non-recommendation of the individualized measurement (P3) for regions (in the case of the Brasília and Cruzeiro AR) in which there is predominance of apart- ment housing without individualized measurement, and the recommendation to strengthen the water supply operator (P18) to a well-structured company (CAESB). These problems suggest the need for making adaptations to the UWC-ES since the model responded reasonably to these cases. In contrast, the indication of the PP for rainwater collection and use (P7), greywater collection, treatment and use (P8), regulation of consumption (P16) and using good practices for water conservation (P19) can be consid- ered appropriate for the case studies, as they try to solve the UWC problem by addressing its cause (j = 4, greater influence of the environmental level). Thus, it can be considered that the devel- oped ES presented acceptable results, in agreement with pre- viously presented adjustment indicators.

4. Conclusions

A computational tool was developed to help select a set of priority projects (PP) to solve the UWC problem. This tool was

called UWC-ES. The tool (UWC-ES) can replace human and financial resources for decision making in UWC. Therefore, it is especially suitable for urban environments where limitations of human and financial resources are important.

The results of the UWC-ES indicated acceptable applicabil- ity and the possibility of using it in other cases. The use of UWC-ES is based on analyzing various pieces of information about the urban environment by an (artificial) UWC expert, which reasonably reproduces the knowledge of several human experts. Thus, the resources required to use the UWC-ES con- sist of efforts to obtain these various pieces of information and, of course, without the experts’ full participation.

Although the characteristics of the problem are appropriate to the approach of the expert system, some obstacles were encountered during the development of the UWC-ES sub- model, including the following: (1) the difficulty of finding specialists willing to collaborate, (2) the existence of diver- gence between the opinions of specialists and (3) the exis- tence of problems in inference, mainly due to the existence of divergence between the opinions of the specialists. Thus, new studies are suggested focusing on changes in methodology in order to minimize the divergence of expert opinions. One possible modification, for example, could be the aggregation of responses from experts with similar academic backgrounds and the assignment of weights to each specialty class.

Acknowledgements

The authors would like to express their gratitude for the financial sup- port from the Brazilian agencies CNPq (Project Nº 556084/2009-8) and CAPES. The authors would like to express their gratitude to the following institutions: Brazilian Federal District’s Regulatory Agency for Water, Energy and Sanitation (ADASA), Water National Agency of the Brazil (ANA), Brazilian Federal District’s Water Supplier and Sanitation Company (CAESB) and Brazilian Federal District’s Planning Company (CODEPLAN).

Disclosure statement

No potential conflict of interest was reported by the authors.

Figure 3. CLIPS development environment and part of the developed ES coding.

Table 3. UWC-MODEL and UWC-ES results for the case studies.

Classification of cases according to the UWC-MODEL

UWC-ES results for the case studies

Case Study j = 1 j = 2 j = 3 j = 4 j = 5 PP1 PP2 PP3 PP4 PP5

Brasilia AR fr fr mo fo mfr P18 P19 P16 P8 P7 Cruzeiro AR fr fr mo fo mfr P18 P19 P16 P8 P7 Estrutural AR mo mo mo fo fr P19 P7 P1 P3 P10 Guará AR mo mo mo fo mfr P19 P3 P1 P8 P7 Lago Norte AR mo mo fo fo fr P1 P7 P8 P6 P10 Park Way AR mo mo fo fo fr P1 P7 P8 P6 P10 Varjão AR fr mo mo fo fr P1 P2 P3 P6 P7

566 W. T. P. D. SILVA AND M. A. A. D. SOUZA

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