Abstract
Purpose
The self-cleaning properties of nanostructured titanium dioxide facade coatings are useful in Singapore's tropical climate. However, its potential maintenance issues need to be determined right at the design stage. The purpose of this paper is to highlight the development of the design for maintainability tool which is a multicriteria design decision score sheet that evaluates the maintainability potential of nano-facade coating applications on high-rise façades with concrete and stonemasonry finishes and curtain walls.
Design/methodology/approach
Quantitative methods (expert and practitioner surveys) are conducted in this research study. Analytic hierarchy process (AHP) and sensitivity analysis were used to develop a robust Design for Maintainability tool.
Findings
Safety measures indicator received the highest weighted score by experts, while the maximizing performance, minimizing risk, minimizing negative environmental impact and minimizing consumption of matter and energy were the top ranking main criteria by both experts and practitioners. The top ranked design for maintainability sub-criteria identified by practitioners and experts were risk management, maintenance considerations, climatic conditions, safety measures, lifecycle cost and maintenance access, sun's path, rainfall intensity, biological growth measures and building age profile.
Originality/value
Most researches on the maintainability of nano-façade coatings uses experimentation to test the durability of nano-façade coatings, while this study focuses on design based empirical data such as establishing and ranking the list of design for maintainability criteria or indicators to minimize future defects and maintenance issues. The design for maintainability tool contributes to the maintainability of nano-façade coatings leading to maximizing its performance while minimizing cost, risks, resource consumption and negative environmental impact.
Keywords
Citation
Conejos, S., Ubando, A. and Chew, M.Y.L. (2022), "Design for maintainability tool for nano-façade coating applications on high-rise facades in the tropics", Built Environment Project and Asset Management, Vol. 12 No. 1, pp. 70-95. https://doi.org/10.1108/BEPAM-04-2020-0078
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited
Introduction
The lack of maintainability consideration on green building technologies in the planning and design stage has led to the study on the maintainability of green building technologies (VGS, nano-façade coating, BIPV and LED Media Wall) in tropical conditions (Chew et al., 2017a, 2018a; Chew and Conejos, 2016). This is primarily due to the issue of incorporating these green features in the effort to be “fashionable” rather than serving its purpose (Snyder, 2008). Another reason why maintaining these green building technologies is a problem is due to the lack of knowledge or expertise of the specific green building technology that is incorporated in the building (Chew et al., 2017b). This research study pertains to the maintainability of nano-facade coating applications on high-rise façades with concrete and stonemasonry finishes and curtain walls. The building envelope is a fundamental aspect of the building maintenance lifecycle due to façade dirt and staining, causing enormous amount of maintenance cost every year. The cleaning process is not only cost intensive but also takes toll on the water resources for the façade maintenance. Facade staining (including leaching and efflorescence) can be prevented through the application of nano-facade coatings. The application of nano-façade coatings will improve facade durability (Graziani et al., 2013), reduce maintenance costs (Benedix et al., 2000), augment the building's performance and energy efficiency while preventing façade cleaning related risks and accidents and ensuring the aesthetic value of the building envelope (Chew et al., 2017a). Thus, the overall building life cycle costs attributable to façade maintenance would be reduced in a more environment-friendly method (Spassiano et al., 2015).
Singapore recognized the nano-facade coating technology as an innovative and eco-friendly green building technology in the Green Mark Scheme. The self-cleaning properties of nano-façade coatings are useful in Singapore's tropical climate. However, as a recent alternative to conventional façade maintenance method, potential maintenance issues need to be determined right at the design stage to ensure its effective performance and sustainability. Most TiO2 research works are mainly experimental studies (e.g. Guan, 2005; Zhang et al., 2010), while few researches have reported the challenges, problems and issues concerning the maintainability of nano-façade applications on concrete and stonemasonry finishes and curtain walls (Chew et al., 2017a) as shown in Plate 1 (refer to dataset link).
This paper highlights the stage 2 of the research study which is the ranking of the level of importance of each identified design for maintainability (DfM) criteria based on practitioner and expert surveys through the use of analytic hierarchy process (AHP) and sensitivity analysis to come up with a robust scoring system. In the stage 1 of this research study, qualitative approaches which includes instrumental case studies, field observation survey and stakeholder interviews were conducted to investigate the various defects occurring in nano-façade coating applications and other maintainability issues. The result of stage 1 was the list of design for maintainability criteria (Chew et al., 2017a) which is an essential by-product in achieving the aim of this research paper, which is the development of a design for maintainability tool which is a multicriteria design decision score sheet that evaluates the maintainability potential of nano-facade coating applications on high-rise building façades.
Nanostructured titanium dioxide façade coatings
Nanostructured titanium dioxide particles are primarily in the 1–100 nanometers size range and are transparent and not inert (Bakker, 2008), which has self-cleaning function that washes away dirt through photocatalytic activity based on UV radiation and hydrophilicity (Chew et al., 2017a). Nano-facade coating is used as an anti-staining coating on building facades to allow ease in maintenance and less maintenance cost than ordinary façade coatings (Midtdal and Jelle, 2013). Nano-façade coating carries with it technical benefits such as the prevention of water smudging on façade (Lee et al., 2003), ensuring façade durability (Quagliarini et al., 2012; Graziani et al., 2013; Kirtay, 2014), acting as self- cleaning agent (Chew et al., 2017a; Synnott et al., 2013; Agrios and Pichat, 2005; Rudic et al., 2015; Bai et al., 2015), acting as air purifier (Chen and Poon, 2009; Hassan et al., 2010), as anti-bacterial agent (Maness et al., 1999) and as algae and macro-organisms remover (Enea 2013; Hashimoto et al., 2005; Katzman, 2006; Beeldens, 2008; De Richter and Caillol, 2011); as well as addressing the global environmental crisis (Spasiano et al., 2015). Its economic benefits focus on maintenance cost savings (Synnott et al., 2013; Midtdal and Jelle, 2013); while its social benefits centers on maintenance safety such as preventing fall from height accidents (Chew et al., 2017a) and contributing to healthy buildings (Bonetta et al., 2013). Although nano-facade coatings have its various benefits, health and environmental concerns such as nanoparticles' toxicity (Petkovic et al., 2011), DNA damage and cancer development (Singh et al., 2009) and similar symptoms caused by asbestos fiber (Pacheco-Torgal and Jalali, 2011) are few of its drawbacks
Design for maintainability (DfM)
The concept of DfM in Singapore started in 1998 as initiated by the Town Councils to provide quality estate management and maintenance services to Housing Development Board residents in their respective towns (PAP- run Town Councils, 2004). In 2015, the DfM roadmap was introduced as a key pillar to support the facility management (FM) sector's transformation in Singapore; and the DfM was introduced “as the practice of integrating operations and maintenance considerations into project planning and design to achieve effectiveness, safety, and economy of maintenance tasks during the lifespan of a facility” (Building and Construction Authority, 2019). In 2018, the Design for Maintainability: Benchmarks for Quality Buildings was published as a reference to enhance high maintainability of buildings through identifying the common maintainability issues and the corresponding requirements for good practices by designers, constructors and facilities managers at the outset of the planning/design stage (Chew et al., 2018b).
Methods
This paper discusses the stage 2 of the research study which highlights the quantitative results of the experts and stakeholders' surveys to develop the DfM tool using AHP and sensitivity analysis. The AHP was used to rank the DfM criteria derived from the stage 1 results of this research study. The identified nano-façade coating defects and issues in the stage 1 were translated into a set of DfM criteria grouped according to the five green maintainability factors such as maximizing performance, minimizing cost, minimizing risk, minimizing negative environmental impact and minimizing consumption of matter and energy (Chew et al., 2017a).
The analytical hierarchical structure of the nano-façade coating is shown in Figure 1, which consists of the goal (Level 0), the main criteria (Level 1), the sub-criteria of the main criteria (Level 2) and the sub-criteria of the sub-criteria of the main criteria (Level 3).
The quantification of the scores for the DfM criteria uses a pairwise comparison among the criteria and sub-criteria. A positive reciprocal matrix is used as shown in Equation (1).
The pairwise comparison valuation coefficient aij can be further defined in terms of the ratio of the weights of the criteria as shown in Equation (3).
To assist in assigning a graded scoring system to the DfM tool, a practitioner survey was conducted among 91 designers and allied professionals in Singapore (Table 1). To gather a reasonable sample size of practitioner survey, a focus group seminar event was conducted among invited and interested practitioners to know about the research and participate in the survey with willingness. The practitioner survey results were then later validated via an expert survey.
The practitioner survey was the highlight of the conducted seminar on the maintainability of green building facades attended by invited and interested built environment professionals and academics. The seminar with lecture and Q&A session and practitioner survey was conducted for four hours and provided continuing professional development (CPD) points as incentive to the invited and interested participants cum practitioner survey respondents. The seminar provides a background of the research project as well as provide educational awareness about the issues and challenges encountered when designing green buildings without considering maintainability at the outset. The 91 practitioners represented government, construction, design/design consultancy, facility management, academe and other related building industry sectors. Most of the practitioners who came from design, construction and facility management backgrounds have worked at medium to large scale firms; have less than 20 years of experience, but have handled over 1.5 million projects. Some of the practitioners assumed managerial positions, while most of them are handling residential to commercial to mixed-use types of building projects.
To validate the practitioner survey results, an expert survey was also conducted among two experts representing the academe and industry, who have relevant knowledge and sound experience on nano-façade coating, act as consultants in the application and testing of nano-façade coating in various projects in Singapore and abroad. Expert 1 is the founder of maintainability of buildings in the tropics and brings with him a wealth of knowledge and experience concerning topics related to maintainability for more than 25 years. Expert 2 offers valuable contribution as a practising facility manager and author of a number of nano-façade coating papers and as a technical engineer of expert 1's consultancy projects. Expert 2 has an eye for the operational maintainability of buildings, while able to link the practical side and the research side with PhD degree in building science.
The practitioner and expert surveys were coded in excel format and the AHP was used to analyze the data. Saaty (1980) primarily pioneered the AHP as a multicriteria decision analysis (MCDA) tool in the late 1970s to facilitate the quantification of priority weights among alternatives. As a decision analysis tool, AHP has been applied in various research projects related to building maintainability (Das et al., 2010); green manufacturing (Chiang et al., 2011); procurement processes (Yang et al., 2012); energy systems (Tan et al., 2013); photovoltaic industry (Tang et al., 2014); water conservation (Zhu and Xu, 2014) and algal cultivation systems (Ubando et al., 2016). A sensitivity analysis technique is further used to evaluate the robustness of the scoring of the DfM tool since the sensitivity analysis has been applied in researches concerning building maintainability (Chew et al., 2004) and sustainable building designs (Heiselberg et al., 2009).
Discussion of findings
The aim of this research study is the development of the DfM tool for nano-façade coatings which involved the ranking of the critical DfM criteria using AHP and sensitivity analysis to determine the robustness of the scoring system.
Survey results from the 91 practitioners
The survey results from 91 practitioners yielded the weight for the level of importance for the five main criteria (Figure 2 in dataset link). The weight was quantified using the geometric mean (Aczel and Saaty, 1983) of the 91 response from survey respondents for the main criteria. One of the main advantages of the geometric mean versus the arithmetic mean is it dissociates any biases of the respondent, thus, it conveys an objective and transparent result. The ranking of the level of importance are: (1) maximizing performance, (2) minimizing risk, (3) minimizing consumption of matter and energy, (4) minimizing cost and (5) minimizing negative environmental impact. Due to the round-off of the decimal point, the minimizing cost and minimizing negative environmental impact criteria are of similar weight at 4.32. However, the results have shown that the minimizing cost criterion was slightly higher compared with the minimizing negative environmental impact criterion.
The weighted result for the level of importance of level 2 sub-criteria (Figure 3 in dataset link), showed that under the maximizing performance criterion, two sub-criteria were identified as the climatic conditions and design considerations. The climatic conditions sub-criterion weighted score of 4.28 is more than the design considerations sub-criterion with a score of 4.03. The four remaining level 1 criteria such as the minimizing cost, minimizing risk, minimizing negative environmental impact and minimizing consumption of matter and energy have individual corresponding sub-criteria which are maintenance considerations, risk management, environmental considerations and water and energy efficiency considerations, respectively. Among the level 2 sub-criteria, the risk management sub-criterion has the highest weight with a score of 4.45, followed by the maintenance consideration sub-criterion with a score of 4.30.
The weighted result for the level of importance of level 3 sub-criteria with a total of 15 sub-criteria (Figure 4 in dataset link) showed that the climatic conditions sub-criterion under the maximizing performance have three level 3 sub-criteria which are the rainfall intensity for building orientation, wind direction and sun's path with weighted scores of 4.29, 4.02 and 4.29, respectively. The design considerations sub-criterion under the maximizing performance has four level 3 sub-criteria which are the urban context/neighborhood, building configuration, types of substrates and levels of application with weighted scores of 4.03, 4.11, 4.13 and 4.14, respectively. The life cycle cost, maintenance access, building age profile and application techniques level 3 sub-criteria are all under the maintenance sub-criterion beneath the minimizing cost criterion have weighted scores of 4.35, 4.36, 4.29 and 4.05, respectively. The pollutant prevention measures and the biological growth measures level 3 sub-criteria under the environmental considerations sub-criterion beneath the minimizing negative environmental impact criterion have weighted scores of 4.13 and 4.29, respectively. The water and energy consumption and savings level 3 sub-criterion under the minimizing consumption of matter and energy criterion and the water and energy efficiency considerations sub-criterion have a weighted score of 4.21. The safety measure level 3 sub-criterion has the highest weighted score of 4.57 under the minimizing risk criterion and risk management sub-criterion followed by the maintenance access and the life cycle cost sub-criterion which ranked second and third, respectively.
Experts' survey results
The experts' survey results have employed a pairwise comparison analysis of the criteria and sub-criteria to quantify its relative importance. The result yielded a normalized weight score for the clusters of criteria and sub-criteria for two experts. For the entire lone criterion under levels 2 and 3, a relative weight of 100% is given to each of this sub-criterion. The relative weight scores of the main criteria from the two experts (Figure 5 in dataset link); the maximizing performance, minimizing risk and minimizing negative environmental impact criteria were the top three ranking main criteria. The weights derived from experts 1 and 2 are shown in Figure 5. The results indicate a different trend for both experts. Expert 1 prioritized minimizing Risk at 40%, followed by maximizing performance at 23%, then followed by minimizing negative environmental impact at 19%. While for expert 2, maximizing performance was prioritized at 38%, followed by minimizing negative environmental impact at 24% and then followed by minimizing risk at 18%. As decision-makers, the difference on the opinions of the two experts with regards to the prioritization of the criteria have led to the resulting weights shown in Figure 5.
The relative weighting of the two sub-criteria under the maximizing performance criterion resulted in similar weights for climatic condition and design considerations sub-criteria based on expert 1, while expert 2, considered the design considerations sub-criterion more important than the climatic condition sub-criterion with relative scores of 83 and 17%, respectively (Figure 6 in dataset link).
The resulting relative weightings of the three sub-criteria under the climatic conditions sub-criterion have shown that the rainfall intensity for building orientation sub-criterion has significantly minor importance compared to the other two criteria such as the sun's path and wind direction sub-criteria having equal relative weightings at 46% based on expert 1. While expert 2, have chosen the rain fall intensity sub-criterion to have the highest relative importance at 49%, followed by the sun's path sub-criterion at 44% relative weight (Figure 7 in dataset link).
The relative weightings for the four sub-criterion under the design considerations sub-criterion have shown that the type of substrates and the levels of application sub-criteria were identified as the top three ranked sub-criteria for both experts. The type of substrates sub-criterion was chosen by both experts as the top ranked criterion for the design considerations sub-criterion, which almost covers half of the relative weight of the design considerations sub-criterion for the two experts (Figure 8 in dataset link).
The experts' survey results on the relative weightings of the four sub-criteria under the maintenance considerations sub-criterion have shown that the ranking of the four sub-criteria are the same for the both experts where life cycle cost is top ranked, followed by maintenance access, then the application technique and lastly the building age profile. Both the life cycle cost and the maintenance access sub-criteria were identified as one of the top three ranking sub-criteria (Figure 9 in dataset link).
The experts' survey results on the relative weightings of the two sub-criteria under the environmental considerations sub-criterion (Figure 10 in dataset link) have shown that expert 1 has considered biological growth measures sub-criterion more important than the pollutant prevention measures (Figure 10a in dataset link). However, expert 2 gave more importance to the pollutant prevention measures than the biological growth measures (Figure 10b in dataset link). Both experts showed different views on the relative importance for both sub-criteria.
In summary, the result of the relative weights from the AHP has yielded to a normalized weight shown in Table 2 for the hierarchical structure of Figure 1. The normalized weights used a relative weight to quantify the weighted scores in the next section. It can also be observed that there are differences in the opinion between the two experts as shown in the varying weights shown in Figures 5–10 which resulted to the normalized weight shown in Table 2. The differences of normalized weights between experts 1 and 2 show the inferences of decision of prioritization of the criteria and sub-criteria for the potential maintainability of nano-facade coating in high-rise buildings.
Developing the design for maintainability tool using sensitivity analysis
The aim of the sensitivity analysis is to evaluate the relative influences of each criterion with the other criterion. The sensitivity analysis approach was previously employed in the AHP method for the evaluation of biomass cultivation method (Ubando et al., 2016). It is an accepted method to establish the relative influences of the criterion.
The overall weighted score is defined by the equation shown below:
The hierarchical structure of the DfM tool for nano-façade coatings is shown in Figure 1, which consists of four parts. First, at the top of the hierarchical structure is the goal of the assessment tool, which is the maintainability of the nano-façade coating. Second, the level 1 main criterion is composed of the five factors such as the maximizing performance, the minimizing cost, the minimizing risk, the minimizing negative environmental impact and the minimizing consumption of matter and energy. Third, the level 1 main criterion is composed of the level 2 sub-criteria. For the maximizing performance main criteria, there are two level 2 sub-criteria such as the climatic condition and design considerations. While for the remaining main criteria such as the minimizing cost, the minimizing risk and the minimizing negative environmental impact criteria; each have individual sub-criteria which are the maintenance considerations, the risk management, the environmental considerations and the water and energy efficiency considerations, respectively. Lastly, the level 3 sub-criteria are found under the level 2 sub-criteria which clearly defines the sub-criteria being evaluated for the maintainability of the nano-façade coatings.
For the climatic condition sub-criteria, there are three level 3 sub-criteria such as the rainfall intensity for building orientation, the wind direction and the sun's path and intensity for building orientation. For the design consideration sub-criteria, the four level 3 sub-criteria are the urban context/neighborhood surroundings, the building configuration, the types of substrates and the levels of application. The maintenance considerations sub-criteria have four level 3 sub-criteria which are the life cycle cost, the maintenance access, the building age profile and the application techniques. The environmental considerations sub-criteria have two level 3 sub-criteria which are the pollutant prevention measures and the biological growth measures sub-criteria. The risk management and the water and energy efficiency considerations sub-criteria only have a sole level 3 sub-criterion, which are the safety measures and the water and energy consumption and savings sub-criteria, respectively.
The sensitivity analysis consists of determining the weightings for only six groups of criteria from the hierarchical structure since some of the criteria have only one sole sub-criteria under it. The sole sub-criteria will inherit the full weight of the preceding criteria. As an example, the safety measures (level 3) sub-criteria will have the full weight from the risk management (level 2) sub-criteria. Consequently, the risk management sub-criteria will get the full weight of the minimizing risk criteria. Similar case is adapted with the water and energy efficiency consideration (level 2) sub-criteria and the water and energy consumption and savings (level 3) sub-criteria. The six groups of criteria analyzed are (1) the five level 1 main criteria, (2) the two level 2 sub-criteria under the maximizing performance (level 1) criteria, (3) the three level 3 sub-criteria under the climatic condition (level 2) sub-criteria, (4) the four level 3 sub-criteria under the design considerations (level 2) sub-criteria, (5) the four level 3 sub-criteria under the maintenance considerations (level 2) sub-criteria and (6) the two level 3 sub-criteria under the environmental considerations (level 2) sub-criteria.
The level 1 main criteria
The results of the sensitivity analysis to the level 1 main criteria where the box-plot results show clearly the median score of the criterion together with the spread of the data (Figure 11 in dataset link). A longer spread of the data indicates that the criteria are relatively sensitive in the varying weightings between the stakeholders. Conversely, a shorter spread implies that the criteria are relatively insensitive of changes in the different priority weightings between stakeholders. The main criterion with the highest median score is the maximizing performance, followed by the minimizing risk, then by minimizing negative environmental impact, then followed by minimizing consumption of matter and energy and lastly followed by minimizing cost. The most sensitive criterion among the five main criteria is the minimizing risk criterion, while the least sensitive criterion is the minimizing negative environmental impact criterion.
The sub-criteria of maximizing performance criterion
The results of the sensitivity analysis to the two sub-criteria under the maximizing performance level 1 criterion showed that the design consideration criterion yielded a relatively higher score compared to the climatic condition criterion as indicated by the comparison of the median score of the two sub-criteria (Figure 12 in dataset link). Due to the sensitivity of the two criteria with the changes of the weightings among the stakeholders, an area of potential score overlap is recognized as illustrated by the spread of the two criteria. This imply that the weight of the two criteria may be equally divided near the range of 0.50 where the top tip of the climatic conditions criterion and bottom tip of the design considerations criterion overlaps. In this case, the overlap region indicates approximately the same weight for the two criteria at 0.50.
The sub-criteria of the climatic conditions sub-criterion
Under the climatic conditions sub-criterion, the analysis result for the three sub-criteria showed that the results indicated that the sun's path and rainfall intensity for building orientation criterion matched the highest score and the least sensitive among the other criteria (Figure 13 in dataset link). A region of overlap has been identified between the rainfall intensity for building orientation and the wind direction criteria as indicated by the spread of data. The rainfall intensity for building orientation criterion was discovered to be relatively sensitive to the change of weighting between stakeholders among the three sub-criterion.
The sub-criteria of the design considerations sub-criterion
The result of the analysis between the four sub-criteria under the design considerations sub-criterion is shown in Figure 14 (refer in dataset link). Among the four sub-criteria, the types of substrates criterion stands-outs among the rest of the criteria having the highest score. The levels of application criterion scored slightly higher compared to the urban context/neighborhood surrounding and the building configuration criteria. In addition, the levels of application criterion have the least spread signifying the least sensitive criterion on varying scenarios. The urban context/neighborhood surrounding and building configuration criteria have an almost identical score and spread indicating almost similar scores for the different scenarios. For a risk-averse decision-maker (pessimist), the levels of application criterion is chosen, as it has the thinnest spread which indicates an unchanging weight. While for a risk-inclined decision-maker (optimist), the types of substrates criterion is selected due to its high scoring weight despite a thick spread of values.
The sub-criteria of the maintenance considerations sub-criterion
The comparison of scores among the sub-criteria of the maintenance considerations sub-criterion is shown in Figure 15 (refer in dataset link). The life cycle cost criterion has scored the highest among the other sub-criteria, but, it is also the most sensitive as indicated by the relatively long spread of data. The maintenance access criterion was accounted to be the least sensitive criterion as described by the relatively short spread of data. The scores of the building age profile and the application techniques criteria have almost the same scores ranging from 0.10 to 0.20.
The sub-criteria of the environmental considerations sub-criterion
The resulting box-plot of the two sub-criteria under the environmental considerations sub-criterion is shown in Figure 16 (refer in dataset link). The biological growth measures criterion was discovered to have a slightly higher score compared to the pollutant prevention measures criterion as recognized by the higher median score. However, a huge region of overlap has been identified between the two sub-criteria as indicated by the range of data. This means that that the score of the two sub-criteria is most likely interchange in the score near 0.50.
The result of all level 3 sub-criteria
The resulting comparative box-plot of all level 3 sub-criteria is shown in Figure 17 (refer in dataset link). The top five ranking criteria are the safety measures, water and energy consumption and savings, biological growth measures, pollutant prevention measures and types of substrates, respectively. The safety measures sub-criterion scored the highest among all other sub-criteria due to the full allocation of the weights coming from the risk management sub-criterion (level 2) and the minimizing Risk criterion (level 1), which is also discovered as the most sensitive criterion in all scenarios. The water and energy consumption and savings sub-criterion also has a high score due to the direct weighting calculation from the water and energy efficiency considerations sub-criterion (level 2) and the minimizing consumption of matter and energy criterion (level 1).
The results reveal that a relatively higher score may be drawn from the sub-criteria with a lesser number of competing sub-criteria in its corresponding level. The five top ranking sub-criteria have lesser competing criteria in its level, thus, the weighting coming from levels 1 and 2 are fully transferred. Since the weighting from levels 1 and 2 are transferred to the level 3 sub-criteria, the spread of the data is also relatively larger for this sub-criteria as the changes of weighting in the level 1 criteria are carried over to the level 3 sub-criteria, which is relatively volatile compared to the level 3 weighting changes. The DfM tool scoring of the level 3 sub-criteria for all scenarios are summarized and shown in Table 4. The darker colored regions indicate a relatively higher score, while the lighter colored regions indicate a relatively lower score.
Summary of findings
The AHP approach employed a two-step approach where the weighted level of importance for each DfM criterion and sub-criteria was quantified initially through the survey results from 91 respondents. Then the normalized weightings were evaluated using an AHP approach employing a pairwise comparison matrix to quantify the relative weightings of each DfM criterion and sub-criteria using experts' opinions. The weighted scores have been computed using the weightings from the results of the 91 respondents and the normalized weightings from the experts. The observed value of the overall weighted score from the two experts varies minimally, but the results significantly differ in the allocation of weightings for each DfM criterion. The safety measures sub-criterion has shown the highest weighted score for both experts. The maximizing performance, minimizing risk and minimizing negative environmental impact criteria were the top three ranking main criteria based on experts, while maximizing performance, minimizing risk and minimizing consumption of matter and energy were the top three choices by practitioners. The top three ranked design for maintainability sub-criteria by practitioners were (1) risk management, (2) maintenance considerations and (3) climatic conditions and water and energy efficiency considerations. While the top three ranked design for maintainability criteria by experts were (1) safety measures, (2) lifecycle cost and maintenance access and (3) sun's path, rainfall intensity, biological growth measures and building age profile. Notably, the safety measures criterion received the highest weighted score for both experts.
A sensitivity analysis has been conducted to evaluate the sensitivity of the scores of the DfM criteria with respect to the changes of weighting among the three decision-makers. The study has identified 19 scenarios to evaluate the scores with the changes in the weightings between three stakeholders. The analysis was based on the defined hierarchical structure of the DfM of the nano-façade coating, consisting of the goal and the three levels of criteria with a total of five criteria for level 1, six sub-criteria for level 2 and fifteen sub-criteria for level 3. The results were expressed in the comparison of box-plots for the criteria to evaluate the score through the median and the sensitivity based on the spread of data. The results showed that the highest scoring level 3 criteria are the ones with less or no competing criteria in the same category and level. The significance of the box-plot is to assess which criteria and sub-criteria are invariant of the changes of weight from various stakeholders. The spread from the box-plot gives the designer an option to prioritize a certain criteria based on a given scenario. A designer can be a risk-averse or risk-incline in choosing to prioritize a specific criterion. Moreover, the same high scoring criteria also have the highest sensitivity to the changes in weighting between decision-makers. This is due to the fact that the full weighting in the level 1 criterion is fully transferred to these level 3 sub-criteria, thus, transferring the volatility of the weightings when changes of weightings are made in level 1. Similar to AHP analysis, the overall highest scoring level 3 sub-criteria is the safety measures and which is also the most sensitive among other sub-criteria.
Design for maintainability (DfM) tool for nano-façade coatings
The DfM tool was developed into a score sheet with best practice guidelines corresponding to each DfM criteria (Figure 18) has been assigned a grading score from the scenario 1 shown in Table 3 (equal weights) which yielded a level 3 sub-criteria shown in Table 4.
As shown in Table 5, the DfM tool consisting of the main criteria and sub-criteria were grouped into the following: (1) climatic conditions which indicates three design criteria such as rainfall, sun's path and wind direction; (2) design considerations which pertains to four design criteria such as urban context/surrounding neighborhood; building configuration, types of substrates and levels of application; (3) maintenance considerations which refers to four design criteria such as the entire nano-facade coating life cycle cost, maintenance access and preventive measures, building age and application techniques; (4) risk management which relates to safety measures; (5) environmental considerations such as biological growth and pollutant prevention measures and (6) water efficiency and savings considerations.
Conclusion and research implications
Forecasting the maintainability of green building technologies such as nano-facade coatings is deemed important today and in the future. This research has evaluated and ranked the set of Design for Maintainability (DfM) criteria based on their level of importance (i.e. Safety measures as the highest important criterion) and highlights its applicability as a guide in ensuring the sustainable application of nano-façade coatings on high rise building facades. This study is useful for Singapore's tropical climate and other countries in the tropics which will benefit in the application of the list of DfM criteria leading to increased maintainability via the DfM tool that provides a direct assessment scoring checklist for green building technologies (e.g. nano-façade coatings). This research is timely as it will lead toward the long-term saving of façade maintenance costs while ensuring its sustainability, effective performance and high aesthetic value, as well as minimizing risks and resource consumption. The practical and theoretical implications of the research is the development of the DfM tool as a checklist and score sheet to guide designers and allied practitioners in the application of nano-façade coatings in tropical areas in order to reduce defect occurrences and maintenance cost as well as ensure the maintainability potential of the nano-façade coating right at the design stage.
Figures
Brief profile of practitioner survey respondents
| Respondents/Practitioners' gender | Sector/Industry | |||||
|---|---|---|---|---|---|---|
| Government | Construction | Professional practice (design/design consultancy) | Academe | Others | Undisclosed | |
| Male | 5 | 10 | 23 | 2 | 4 | 0 |
| Female | 2 | 19 | 15 | 0 | 1 | 1 |
| Unspecified | 0 | 0 | 0 | 0 | 0 | 9 |
| Total | 7 | 29 | 38 | 2 | 5 | 10 |
| Percentages (%) | 7.69% | 31.87% | 41.76% | 2.20% | 5.49% | 10.99% |
| Respondents/Practitioners | Company | ||||
|---|---|---|---|---|---|
| Small | Medium | Large | Others | Undisclosed | |
| Male | 9 | 11 | 23 | 0 | 1 |
| Female | 8 | 11 | 18 | 1 | 0 |
| Unspecified | 2 | 3 | 2 | 0 | 2 |
| Total | 19 | 25 | 43 | 1 | 3 |
| Percentages (%) | 20.88% | 27.47% | 47.25% | 1.10% | 3.30% |
| Respondents/Practitioners | Age | ||||
|---|---|---|---|---|---|
| Below 30 | 31–40 | 41–50 | Above 50 | Undisclosed | |
| Male | 7 | 25 | 11 | 1 | 0 |
| Female | 10 | 24 | 4 | 0 | 0 |
| Unspecified | 0 | 0 | 0 | 0 | 9 |
| Total | 17 | 49 | 15 | 1 | 9 |
| Percentages (%) | 18.68% | 53.85% | 16.48% | 1.10% | 9.89% |
| Respondents/Practitioners | Project costs handled | ||||
|---|---|---|---|---|---|
| Below $500K | 501K – $1M | 1.01M – $1.5M | Over $1.5M | Undisclosed | |
| Male | 5 | 4 | 3 | 30 | 2 |
| Female | 5 | 1 | 7 | 20 | 5 |
| Unspecified | 0 | 2 | 0 | 5 | 2 |
| Total | 10 | 7 | 10 | 55 | 9 |
| Percentages (%) | 10.99% | 7.69% | 10.99% | 60.44% | 9.89% |
| Respondents/Practitioners | Years of experience | ||||
|---|---|---|---|---|---|
| Below 20 | 21–30 | 31–40 | Over 40 | Undisclosed | |
| Male | 36 | 5 | 1 | 0 | 2 |
| Female | 35 | 1 | 0 | 0 | 2 |
| Unspecified | 5 | 1 | 0 | 0 | 3 |
| Total | 76 | 7 | 1 | 0 | 7 |
| Percentages (%) | 83.52% | 7.69% | 1.10% | 0.00% | 7.69% |
| Respondents/Practitioners | Position | ||||
|---|---|---|---|---|---|
| Supervisory | Managerial | Principal/Owner | Others | Undisclosed | |
| Male | 4 | 15 | 5 | 18 | 2 |
| Female | 7 | 6 | 0 | 21 | 4 |
| Unspecified | 1 | 3 | 0 | 2 | 3 |
| Total | 12 | 24 | 5 | 41 | 9 |
| Percentages (%) | 13.19% | 26.37% | 5.49% | 45.05% | 9.89% |
| Respondents/Practitioners | Types of building projects | ||||
|---|---|---|---|---|---|
| Residential | Industrial | Commercial | Others | Undisclosed | |
| Male | 11 | 4 | 17 | 11 | 1 |
| Female | 9 | 4 | 12 | 12 | 1 |
| Unspecified | 1 | 1 | 2 | 3 | 2 |
| Total | 21 | 9 | 31 | 26 | 4 |
| Percentages (%) | 23.08% | 9.89% | 34.07% | 28.57% | 4.40% |
The overall normalized weights of the AHP for the 2 experts
| Goal | Level 1 | Level 2 | Level 3 | Expert 1 | Expert 2 |
|---|---|---|---|---|---|
| Green façade maintainability | Maximizing performance | Climatic conditions | Rainfall intensity for building orientation | 1.05% | 3.08% |
| Wind direction | 5.24% | 0.49% | |||
| Sun's path and intensity for building orientation | 5.24% | 2.75% | |||
| Design considerations | Urban context/Neighborhood surroundings | 2.46% | 3.38% | ||
| Building configuration | 1.10% | 6.64% | |||
| Types of substrates | 5.51% | 16.63% | |||
| Levels of application | 2.46% | 5.00% | |||
| Minimizing cost | Maintenance considerations | Life cycle cost | 4.31% | 5.87% | |
| Maintenance access | 1.49% | 1.59% | |||
| Building age profile | 0.50% | 0.42% | |||
| Application techniques | 0.86% | 0.79% | |||
| Minimizing risk | Risk management | Safety measures | 40.09% | 17.62% | |
| Minimizing negative environmental impact | Environmental considerations | Pollutant prevention measures | 3.18% | 18.27% | |
| Biological growth measures | 15.90% | 6.09% | |||
| Minimizing consumption of matter and energy | Water and energy efficiency considerations | Water and energy consumption and savings | 10.60% | 11.38% | |
| Total weight | 100.00% | 100.00% | |||
The scenario analysis for the distribution of weights among stakeholders
| Scenarios | Expert 1 | Expert 2 | Practitioners survey | Total weights |
|---|---|---|---|---|
| 1 | 33.33% | 33.33% | 33.33% | 100.00% |
| 2 | 90.00% | 5.00% | 5.00% | 100.00% |
| 3 | 80.00% | 10.00% | 10.00% | 100.00% |
| 4 | 70.00% | 15.00% | 15.00% | 100.00% |
| 5 | 60.00% | 20.00% | 20.00% | 100.00% |
| 6 | 50.00% | 25.00% | 25.00% | 100.00% |
| 7 | 40.00% | 30.00% | 30.00% | 100.00% |
| 8 | 5.00% | 90.00% | 5.00% | 100.00% |
| 9 | 10.00% | 80.00% | 10.00% | 100.00% |
| 10 | 15.00% | 70.00% | 15.00% | 100.00% |
| 11 | 20.00% | 60.00% | 20.00% | 100.00% |
| 12 | 25.00% | 50.00% | 25.00% | 100.00% |
| 13 | 30.00% | 40.00% | 30.00% | 100.00% |
| 14 | 5.00% | 5.00% | 90.00% | 100.00% |
| 16 | 10.00% | 10.00% | 80.00% | 100.00% |
| 16 | 15.00% | 15.00% | 70.00% | 100.00% |
| 17 | 20.00% | 20.00% | 60.00% | 100.00% |
| 18 | 25.00% | 25.00% | 50.00% | 100.00% |
| 19 | 30.00% | 30.00% | 40.00% | 100.00% |
The resulting GFMA scores for each level 3 sub-criteria for all scenarios
| Scenarios | RIBO | WD | SP | UC/NS | BC | TS | LA | LCC | MA | BAP | AT | SM | PPM | BGM | WECS | Total score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3.56% | 3.13% | 4.80% | 3.13% | 3.11% | 7.09% | 3.54% | 5.67% | 2.44% | 1.31% | 1.55% | 26.53% | 9.36% | 10.97% | 13.82% | 100.00% |
| 2 | 1.43% | 4.95% | 5.21% | 2.57% | 1.34% | 5.75% | 2.61% | 4.55% | 1.63% | 0.60% | 0.96% | 38.05% | 4.06% | 15.21% | 11.08% | 100.00% |
| 3 | 1.82% | 4.65% | 5.16% | 2.67% | 1.59% | 6.00% | 2.77% | 4.78% | 1.77% | 0.70% | 1.05% | 36.02% | 4.96% | 14.50% | 11.56% | 100.00% |
| 4 | 2.20% | 4.34% | 5.10% | 2.77% | 1.87% | 6.24% | 2.93% | 5.00% | 1.91% | 0.82% | 1.15% | 33.98% | 5.87% | 13.77% | 12.05% | 100.00% |
| 5 | 2.58% | 4.01% | 5.03% | 2.87% | 2.17% | 6.48% | 3.09% | 5.20% | 2.05% | 0.94% | 1.26% | 31.95% | 6.80% | 13.03% | 12.53% | 100.00% |
| 6 | 2.96% | 3.69% | 4.95% | 2.97% | 2.50% | 6.71% | 3.25% | 5.39% | 2.19% | 1.07% | 1.37% | 29.92% | 7.74% | 12.27% | 13.01% | 100.00% |
| 7 | 3.32% | 3.35% | 4.86% | 3.07% | 2.86% | 6.94% | 3.42% | 5.56% | 2.34% | 1.21% | 1.48% | 27.88% | 8.71% | 11.49% | 13.50% | 100.00% |
| 8 | 3.40% | 0.79% | 3.20% | 3.46% | 6.00% | 14.85% | 4.83% | 5.88% | 1.71% | 0.54% | 0.89% | 18.95% | 16.78% | 6.97% | 11.75% | 100.00% |
| 9 | 3.63% | 1.14% | 3.60% | 3.49% | 5.40% | 13.20% | 4.64% | 5.88% | 1.83% | 0.66% | 0.99% | 20.29% | 15.35% | 7.80% | 12.11% | 100.00% |
| 10 | 3.75% | 1.52% | 3.94% | 3.48% | 4.84% | 11.68% | 4.43% | 5.87% | 1.95% | 0.78% | 1.10% | 21.63% | 13.97% | 8.57% | 12.48% | 100.00% |
| 11 | 3.80% | 1.94% | 4.24% | 3.42% | 4.32% | 10.28% | 4.20% | 5.83% | 2.08% | 0.92% | 1.22% | 22.96% | 12.64% | 9.30% | 12.84% | 100.00% |
| 12 | 3.76% | 2.38% | 4.49% | 3.34% | 3.84% | 8.99% | 3.96% | 5.78% | 2.21% | 1.06% | 1.34% | 24.30% | 11.37% | 9.97% | 13.21% | 100.00% |
| 13 | 3.66% | 2.83% | 4.69% | 3.22% | 3.39% | 7.82% | 3.71% | 5.72% | 2.35% | 1.21% | 1.47% | 25.64% | 10.14% | 10.58% | 13.58% | 100.00% |
| 14 | 4.49% | 3.68% | 4.84% | 2.66% | 2.77% | 3.29% | 2.92% | 4.97% | 4.32% | 3.60% | 3.28% | 22.57% | 8.33% | 9.62% | 18.64% | 100.00% |
| 15 | 4.34% | 3.60% | 4.87% | 2.77% | 2.86% | 3.84% | 3.04% | 5.25% | 3.96% | 3.12% | 2.93% | 23.27% | 8.51% | 9.86% | 17.79% | 100.00% |
| 16 | 4.19% | 3.51% | 4.88% | 2.86% | 2.93% | 4.44% | 3.16% | 5.46% | 3.61% | 2.67% | 2.60% | 23.97% | 8.69% | 10.09% | 16.94% | 100.00% |
| 17 | 4.02% | 3.42% | 4.88% | 2.94% | 3.00% | 5.08% | 3.27% | 5.61% | 3.27% | 2.25% | 2.29% | 24.67% | 8.87% | 10.33% | 16.09% | 100.00% |
| 18 | 3.85% | 3.31% | 4.86% | 3.02% | 3.05% | 5.79% | 3.38% | 5.69% | 2.95% | 1.87% | 2.00% | 25.36% | 9.06% | 10.57% | 15.24% | 100.00% |
| 19 | 3.68% | 3.21% | 4.83% | 3.09% | 3.09% | 6.55% | 3.48% | 5.70% | 2.64% | 1.53% | 1.72% | 26.06% | 9.24% | 10.81% | 14.39% | 100.00% |
Note(s): RIBO- Rainfall intensity for building orientation; WD- Wind direction; SP- Sun's path; UC/NS- Urban context/neighborhood surroundings; BC- Building configuration; TS- Types of substrates; LA- Levels of application; LCC- Life cycle cost; MA- Maintenance access; BAP- Building age profile; AT- Application techniques; SM- Safety measures; PPM- Pollutant prevention measures; BGM- Biological growth measures; WECS- Water and energy efficiency and savings
Design for maintainability tool checklist for TiO2 facade coatings
| Name of building and location |
| Building Profile |
| Instructions: please refer to the following Guide Statements when assessing the nano-façade coating applied to high rise building. The scoring sheet indicates 1- strongly disagree; 2- disagree; 3 – neutral; 4 – agree and 5 – strongly agree. Results of this assessment will be presented in the GFMA model section. Ranking categories are as follows: Level 1–0% to 40% indicates low maintainability potential; Level 2–41% to 60% indicates adequate maintainability potential; and Level 3–61 to 100% indicates high maintainability potential |
| Factors | Design categories | Design criteria | Design for maintainability (DfM) guide statements | 1 | 2 | 3 | 4 | 5 | ✓ |
|---|---|---|---|---|---|---|---|---|---|
| Maximizing performance | Climatic conditions | Rainfall intensity for building orientation | TiO2 coating application is located in areas where there is even distribution of heavy rainfall to allow the ease of rainwater run-off to ensure the full performance of the self-cleaning agent | ||||||
| Sun's path and intensity for building orientation | TiO2 coating is installed in orientations with the maximum amount of solar energy such as the East-West facing façades which are generally exposed to a higher intensity of sunlight to strengthen the anti-staining property of the coating | ||||||||
| Wind direction | TiO2 coating is applied where there is enough wind direction to propel the rainfall rather than divert it from the location | ||||||||
| Design considerations | Urban context/neighborhood surroundings | TiO2 coating applications were done based on considerations of the height of adjacent/neighboring buildings, roof protrusion or shadings. The location of TiO2 coating applications were not shaded and covered by areas or buildings which will prevent sufficient sunlight and rain from reaching the coating | |||||||
| Building configuration | TiO2 coating application took into consideration the building's shape as it affects the rainfall flow (run-off); avoiding recessed or irregularly shaped column façades and recessed building shapes as the shape disrupts the even and regular rainfall pattern resulting in the irregular self-cleaning function on the façade. Note: In cases where the coating is applied on surfaces that are horizontal or only slightly inclined (e.g. roofs, canopies), a gradient of at least 30° is required for effective performance | ||||||||
| Types of substrates | The different types of substrates, such as glass, concrete, painted aluminum cladding, masonry, stone and other façade surfaces, were considered when deciding to apply TiO2 façade coating. Smooth surfaces provide better adhesion for TiO2 coating and lead to its higher durability and longer period of performance | ||||||||
| Levels of application | In considering the different levels of the building, TiO2 coatings were applied on levels with a high amount of sunlight intensity. Areas near the ground level often have minimal sunlight, which will decrease the photocatalytic activity of the TiO2 coating, while the higher levels of the façade receives more sunlight leading to higher photocatalytic activity | ||||||||
| Minimizing cost | Maintenance considerations | Life cycle cost | Maintenance considerations during the entire lifespan of whole TiO2 coating application cost from design stage to operations and maintenance stages were deliberated during the process. Since the expected service life of TiO2 coating is usually ten years, did the building have undergone TiO2 re-application within the 5–10-year period to ensure the efficient performance of the TiO2 coatings? | ||||||
| Maintenance access and frequency of inspection and maintenance | TiO2 applications were located in places where there is ample design capacity in public spaces, standard maintenance access and unobtrusive plan layout for prevention from possible abrasions caused by heavy human and goods traffic flow. Areas where abrasion from human traffic flow occurs are not suitable for TiO2 coating as it may be easily be scraped off | ||||||||
| Building age profile | TiO2 applications were done on newer buildings that are not more than 10 years of age. As the building ages, its building surface becomes more porous and the adhesion of TiO2 coating on the existing surface will be an issue. Old substrates might lead to premature failure of the coating due to cracking, dusts which cannot be removed during surface preparation etc. | ||||||||
| Application techniques | Application of photocatalytic coating is very critical for its performance; instructions in application manual should be strictly followed. Surface preparation of the substrate is very important in ensuring the surface is dust free to receive the coating. The installers or operators have not touched the coated surface within 48 h of application to prevent the tampering of coating | ||||||||
| Minimizing risk | Risk Management | Safety measures (incl. fire safety and quality of workmanship) | Consideration of safety measures during construction, application and maintenance in the design stage of the building such as provision of access, maintenance and repair methods were provided | ||||||
| Minimizing negative environmental impact | Environmental considerations | Pollutant prevention measures | TiO2 application is not in close proximity to the road which exposes the coating to pollutants from vehicles. TiO2 coating is likely to become thinner over time due to the exposure to acid rain and heavy raining, thus façades located near roads and construction sites are prone to pollutants and dusts emitted from vehicles and construction sites | ||||||
| Biological growth measures | TiO2 application is not in close proximity to landscapes and highly vegetated and dusty areas which will encourage algae growth. Water run-off and ponding issues were determined prior to deciding which areas/locations are suitable for TiO2 application | ||||||||
| Minimizing consumption of matter and energy | Water and energy considerations | Water and energy efficiency and savings | The management and conservation of energy and material usage in a building (e.g. water, electricity, waste, etc.) were considered right at the design stage of the building, and that water efficiency and savings are a must when considering the location and application of TiO2 coatings |
Note
Note: For Plate 1 and Figures 2–17, please refer to dataset link: DOI: 10.13140/RG.2.2.22339.43046
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