THE EFFECT OF MATERIAL COLORS ON THE EVALUATION OF STAINS ON EXTERNAL BUILDING WALLS

　

Shinobu ISHIGAMI

Reseach Assoc., Dept. of Architecture, Chiba Institute of Technology, Dr. Eng.

E-mail Address: ishigami@cc.it-chiba.ac.jp

　

　

ABSTRACT

The aim of this paper is to physically examine indications of negative perceptions of stains which occur on external building walls. We hope to indicate physically sensitivity to stains using the lightness difference between stained and unstained materials in optical measurements. In the case of charcoal as a contaminant, we can indicate more precisely sensitivity to stains with a multiple regression equation which uses both lightness difference and a quantity of changing chromatic factors.

　

　

1. INTRODUCTION

　

To indicate the degree of pollution on materials physically, it is assumed that the optical measurement value is good.[1] The color difference between stained and unstained materials is most useful to measure, and it is often used as a standard measurement of pollution on materials. However, there are several reports that it is not always universal, because factors of surface design "color, pattern and texture" or contaminants strongly influence the appearance of stains.[2]

On the other hand, the authors of this paper have studied stains as a part of their investigation into the aesthetic maintenance of external building walls. We examined the relationship between color difference and sensory value on the basis of our experiment [3] on the effect of material color on the evaluation of stains, but there was not a strong correlation. We examined multiple regression analysis to calculate the variable of negative perception of stains on the basis of our experiment [4] on the influence of observation distance on the appearance of stains. For the optical quantity, Y value change rate was adopted as the explanation variable, but color difference was not adopted. Therefore, we concluded that color difference cannot be used to gain an indication of negative feelings towards stains on material surfaces. Accordingly, this paper sets out to examine the physical value which can contribute to an indication of a feeling of visual unpleasantness of stains on the basis of our past experiment. In particular, we are examining stains which have been caused by pollution and falling water.

Table 1 shows the color specimens offered in the experiment, and figure 1 represents the relationship between the lightness of material surfaces and the unpleasantness sensory values as a result of the sensory test. The sensory values of stains in this study were converted into the ordinal scale data of each series. They were adjusted as standard distribution scale data with standard constitution theory. We examined whether this agreed with the judgment of each panelist in each sensory test, and it was identified as agreeing to all except the risk factor of 5% with the official approval method of Kendall and Friedman.　

The following 3 definitions of a "stain" have been proposed. The first is the actual contamination itself. The second is optical measurement value changed by the pollution.　The third is human visual perception. In this report, we use the third definition of "stains".

Table-1　Specimen color

　

<<< Figure-1　Result of sensory testing >>>

　

　

2. RELATIONSHIP BETWEEN THE SENSORY VALUE OF STAINS AND THE PHYSICAL MEASUREMENT OF COLOR

　

Table 2 presents the coefficient of correlation between the sensory value and the color difference "�凾d*", and the coefficient of correlation between the sensory value and the lightness difference "�凾k*" and between the sensory value and the value of changing of chromatic factors. We have presented "�凾b_Ｅ^* " (＝�凾�^*2＋�凾�^*2) jointly in table 2. Color difference consists of lightness difference and the value of changing chromatic factors. It is assumed color difference is good to indicate pollution degree. But, according to table 2, as humans evaluated stains on the basis of feeling of unpleasantness as in this experiment, the correlation with color difference was not recognized except in cases of stains by carbon. We considered the reason to be the following.

　

　

Table-2 Relationship between sensory value and physical value "single function"

Color difference is represented by the following expression ( 1 ) . As it is developed, expression ( 2 ) is derived from expression ( 1 ), and we understand that the value of changing chromatic factors is a constitution element of color difference. Generally, according to table 2, the physical value of good correlation with sensory value is lightness difference.　And, because the correlation with the value of changing chromatic factors is low in the case of Kanto-loam soil and volcanic ash of Mt. Sakurajima as contaminants, the correlation with color difference formed on the basis of �凾b_Ｅ^* also decreases.

　

　　�凾d^* ＝（ �凾k^*2 ＋ �凾�^*2 ＋ �凾�^*2 ）^1/2 -------------------( 1 )

　　　　 ＝〔 �凾k^*2｛（�凾�^*2　＋�凾�^*2 ）^1/2 ｝² 〕^1/2

　　　　 ＝（ �凾k^*2 ＋ �凾b_E^＊2 ）^1/2 ---------------------------( 2 )

　

According to figure 2, differences are recognized in the ratios of constitution elements of color difference by the chroma of original specimens, and, as the ratio of chroma increases, the contribution of �凾b_Ｅ^* increases. Furthermore, this phenomenon becomes clear in figure 3where the contribution ratio of �凾b_Ｅ^* increases with the increasing chroma of the original specimen. Accordingly, as the ratio of �凾b_Ｅ^* becomes large, color difference value becomes very large in the case of high chroma, and the correlation with the sensory value decreases.

　

　

<<< Figure-2　Relationship between chroma of original specimens and

　　sensory value >>>

　

<<< Figure-3　Relationship between chroma of original specimen and

　　 ratio of �凾bＥ* ingredient >>>

　

　

On the other hand, when the color is located in the upper range of "color solid" like white, it thickens when contamination is present.　But, in the case of a polluted specimen of high chroma color or low lightness color, the lightness of the specimen rises or the chroma decreases, except in the case of carbon where the lightness of the specimen decreases.

　 On this account, it is difficult to indicate the sensory value with the usual color difference expression because of the various changes in color after pollution. Color difference also shows a good correlation with physical values in the case of carbon as the contaminant. For an explanation, we suggest the following.　Because all lightness of specimens decreases, the direction of change is the same. Because �凾b_Ｅ^* is fewer than in the case of other contaminants if the chroma of the original specimen is various, the value of �凾b_Ｅ^* is not much larger than the value of lightness difference.

　

　

3.　AN ESTIMATE OF SENSORY VALUE BY PHYSICAL VALUE

　

In the previous section , we explained that color difference was not always universal as a physical value to evaluate visual unpleasantness perception with stains on materials. But, we still cannot deny the effectiveness of the theory of color difference that is composed of both �儉* and �凾b_E^* .　Accordingly, we applied �儉* and �凾b_E^* as explanation variables and a sensual value as a purpose variable, and applied multiple regression analysis to them.　The result is presented in table 3.

Lightness difference was adopted in all series as an explanation variable, but �凾b_E^* was not adopted in any series in the case of Kanto-loam soil or volcanic ash of Mt. Sakurajima as the contaminant, Accordingly, lightness difference was only able to estimate sensual value in color information.

According to figure 4 that presents the relationship between lightness difference and sensory value, the change of sensory value increases as the lightness difference decreases in the case of Kanto loam or volcanic ash of Mt. Sakurajima as the contaminants. And, as the lightness difference increases, the change in the sensual value becomes slow.　Table 3 presents a coefficient value larger than the mono correlation coefficient function in the application of sensory value and lightness difference to logarithm function. On the other hand, both lightness difference and �凾b_E^* were adopted as explanation variables in the case of carbon. And, we were able to estimate a quantity of sensitivity with the multiple regression equation ( f ) which was composed of both physical values.

　

　 ｆ = ａ ＋ ｂ₁ ･ �� L * ＋ ｂ₂ ･ �� C _E * ----------------------（３）　

　　　 　ａ：number clause　　　　 ｂ₁, ｂ₂：correlation coefficient

　

At　this　stage,　if we look at table 3　on the basis of each measurement value in　

"Ｌ^*ａ^*ｂ^* color system" being equivalent, we can see that �凾k^* powerfully influences the estimated expression, because ｂ₁ of the estimated expression ( f )　is much larger than ｂ₂. Furthermore, because the chroma of　the original specimen is low and the influence degree of "�凾b_E^* ingredient" is low, as we evaluate stains with materials near monochrome color only, we consider that the ratio of ｂ₁ to ｂ₂ becomes small.　

　　According to figure 4, the sensual value suddenly becomes large in the case of Kanto loam and volcanic ash of Mt. Sakurajima as the contaminants, but, in the case of carbon, it is shown that the sensual value of stains characteristically increased in proportion to a change of lightness difference.　

The numbered clauses presented in table 3 and figure 4 devises the following check points, and we need to optimize them.　Accordingly, it is an expression that shows an

approximate tendency. They are "Increase range of color specimen", "Set up absolute of

sensual value" and　"Consider observation conditions".

　

　

Table-3　Result of multiple regression analysis

　

　

<<< Figure-4　Relationship of between lightness difference and sensual value,　　　　　　　　　　　　　　 and expression of prediction of sensory value >>>

　

　

　

　

4. CONCLUSION　

　

As a physical value to represent perception of visual unpleasantness with stains, color difference is not always reliable.　In optical measurement, we can indicate sensory value with lightness difference.　In the case of carbon contamination, we can indicate more precisely the sensory value of stains with a multiple regression equation ( 3 ) which uses both lightness difference and changing chromatic factors.

　

　

5. MEMO

This paper was wrtten by Dr. Shinobu ISHIGAMI with Prof. Dr. Hirotake IKENAGA.

　

　

　

REFERENCES

　

[1] H. ONO, H. Baba and M. YOSHIOKA : Presentation of Index and A Method for Evaluating Stains on Polymeric Floor Finishing Materials, Journal of Structural and Constraction Engineering ( Transaction of AIJ ) No.356,　pp9-pp15,　1985.10

　

[2]　T. SHIIRE, Y. KITSUTAKA and S. KAZAMA : Indication Method of dirt on Concrete Surface Part 2, Summaries of Technical Papers of Annual Meeting of AIJ, pp355-pp356, 1980.9

　

[3] S. ISHIGAMI and H. IKENAGA : A Study on The Building Stains ( Relation between The Color of Material and Appearance of Stains ),　Summaries of Technical Papers of Annual Meeting of AIJ, pp95-pp96, 1989.10

　

[4]　H. IKENAGA, S. ISHIGAMI and N. NISHIYAMA : Influence of Observation Distance on The Appearance for Stains on External Building Walls, Journal of Structural and Constraction Engineering　( Transaction of AIJ ) No.465,　pp19-pp26,　1995.11