SOME OBSERVATIONS OF THE SPAB RESEARCH REPORT 2. THE SPAB BUILDING PERFORMANCE SURVEY 2016 INTERIM REPORT: 2017

A guest post by Graham Coleman.

SOME OBSERVATIONS OF THE SPAB RESEARCH REPORT 2. THE SPAB BUILDING PERFORMANCE SURVEY 2016 INTERIM REPORT: 2017

Introduction:

The report ‘The SPAB Building Performance Survey 2016 Interim Report: 2017’ was first made available last year. Having first taken a quick glance at its contents it became apparently clear there were obvious discrepancies in the included tables relating to relative humidity (RH) and absolute humidity (AH) for the 3 properties under investigation.

A more in-depth inspection of the data in the tables has revealed errors and flaws which may reflect on the recorded information and interpretation of results.

The data recorded in the report are given as ‘averages’. It is therefore very important to appreciate that an average is:

“Number that is obtained by adding two or more amounts and dividing the total by the number of amounts”

Thus the average of the sum of 1 to 5 is 3: the spread is 1 and to 5. It is important to note that there is a spread around the average. Statistically this spread is important in that they will distinguish between natural variations and real differences

Be aware that there will be natural variations in data; thus it is imperative that the data shows a significant difference to identify any real change

Table 2:

Given there would distinctly be a spread around the above figures then there is no significant differnce between 2012 – 2016 for each sensor. Also note RH is temperature dependent thus it can simply reflect a change in temperature and nothing else

S1: Mean: 65.5% Min. 64% Max. 66%
S2: Mean: 71.3% Min. 71% Max. 72%
S3: Mean: 77.2% Min. 75% Max. 80%
S4: Mean: 81.8% Min. 79% Max. 84%

Note the largest spread is in those sensors towards the outer part of the wall, ie, closest to the external variable atmospherics.

Table 3:

Note 13 potential data sets; there are 4 omissions for the external data (no data – left blank). To obtain the average the 4 omissions are ignored and the total remaining divided – correctly – by 9

Table 5:

Note: 13 potential data sets for the external data; there are 4 recorded as 0.00. It is impossible to get an AH of 0.00 in normal earth atmosphere. Looking at Table 3 these 0.00’s are clearly ‘absence of data’ yet they have been included in the total of 13 to get the average. This makes a nonsense of the average and distinctly reflects poor data handling.

Table 4:

Given there would distinctly be a spread around the above figures then there is no significant difference between 2013 – 2016 for each sensor.

S1: Mean: 9.80 Min. 9.56 Max. 9.94
S2: Mean: 9.74 Min. 9.42 Max. 9.92
S3: Mean: 10.25 Min. 9.69 Max. 10.71
S4: Mean: 10.01 Min. 9.65 Max. 10.58

Note again the largest spread is in those sensors towards the outer part of the wall,ie, closest to the external variable atmospherics.

Table 5 (mis-numbered in report):

Average pre-insulation data collected over only 4 weeks in January/February 2011 (winter). This is being compared with an annual average (over approximately 52 weeks) 2012 to 2016. This is extremely bias scientific analyses – it therefore becomes irrelevant.

2012 – 2016:

S1: Mean: 6.48 Min. 6.33 Max. 6.85
S2: Mean: 5.09 Min. 5.00 Max. 5.16
S3: Mean: 4.04 Min. 3.08 Max. 4.24
S4: Mean: 4.79 Min. 4.62 Max. 5.11

Note again the largest spread is in those sensors towards the outer part of the wall,ie, closest to the external variable atmospherics.

Table 6:

Note: 13 potential data sets for the external data; there are 4 recorded as 0.00. Looking at Table 3 these 0.00’s are ‘absence of data’ yet they have again been included in the total of 13 to get the average. This again makes a nonsense of the average and distinctly reflects poor data handling.

Figure 5:

The report records an average internal RH of 69.33%. This figure suggests that there is likely to be an internal atmospheric moisture problem – if the recorded data is correct. This very likely
to influence the recorded data through the wall

Table 8:

Given there would distinctly be a spread around the above figures then there is no significant difference between 2012 – 2016 for each sensor. Also note RH is temperature dependent thus it can simply reflect a change in temperature and nothing else

S1: Mean: 64.8% Min. 63% Max. 68%
S2: Mean: 88.0% Min. 85% Max. 90%
S3: Mean: 93.3% Min. 90% Max. 96%
S4: Mean: 96.8% Min. 96% Max. 98%

Table 9:

Note 12 potential data sets; there are 2 omissions for the external data (no data – left blank). To obtain the average the 2 omissions are ignored and the total remaining divided – correctly – by 10 for the average.

Table 10:

Given there would distinctly be a spread around the above figures then there is no significant difference between 2013 – 2016 for each sensor.

S1: Mean: 9.34 Min. 9.15 Max. 9.64
S2: Mean: 10.59 Min. 10.04 Max. 11.13
S3: Mean: 10.91 Min. 10.24 Max. 11.49
S4: Mean: 10.68 Min. 10.17 Max. 11.04

Table 11:

Note: 12 potential data sets for the external data; there are 2 recorded as 0.00. It is impossible to get an AH of 0.00 in normal earth atmophere. Looking at Table 9 these 0.00’s are ‘absence of data’ yet they have been included in the total of 12 to get the average. This makes a nonsense of the average and distinctly reflects poor data handling.

Also from the recorded data:

Month RH AH g/m3 Temperature ºC
September 88.28 0.53 ?!!
October 89.01 2.52 ?!!
November 90.19 3.06 ?!!

To obtain AH averages of this order given the RHs’, the average temperatures would have to be so absurdly low as to be completely unbelievable.

Table 12:

The average pre-insulation data was obtained over 2 weeks only in March (towards end of winter) 2011 then compared to the FULL ANNUAL data (around 52 weeks) AVERAGE 2012-2016. This can only be considered an unrealistic comparison (eg, ‘apples to pears’) and is meaningless.

Table 15:

Given there would distinctly be a spread around the above figures then there is no significant difference between 2013 – 2016 for each sensor. Also note RH is temperature dependent thus it can simply reflect a change in temperature and nothing else

S1: Mean: 77.7% Min. 77% Max. 78%
S2: Mean: 90.3% Min. 89% Max. 91%
S3: Mean: 96.7% Min. 95% Max. 99%

Note the largest spread is in those sensors towards the outer part of the wall,ie, closest to the external variable external atmospherics.

The recorded average RHs for sensor 4 of 110% and 112% are not possible and are well outside the reported operating parameters of +-3%: this strongly suggests faulty loggers.

Figure 34:

The minimum external RH data is recorded as 0.00%. This is an absurd figure and indicates a total absence of water vapour in the atmosphere – nonsense.

Table 16:

Logger S4 records consistently an RH average of 111-112%. RHs’ of this order do not exist: they are likely to indicate a faulty logger. The data is well outside the reported operating parameter of +-3%.

Table 17:

Given there would distinctly be a spread around the above figures then there is no significant differnce between 2013 – 2016 for each sensor.

S1: Mean: 11.98 Min. 11.56 Max. 12.24
S2: Mean: 12.97 Min. 12.73 Max. 13.32
S3: Mean: 12.76 Min. 12.60 Max. 12.91
S4: Mean: 12.29 Min. 11.75 Max. 13.05

Note the largest spread is in those sensors towards the outer part of the wall,ie, closest to the external variable external atmospherics.

Table 18:

The external average external AH for April is given as 2.33

So for April: RH=93.99% AH = 2.33 g/m3 Temperature = ?!!

To obtain an AH average of 2.33 given the RH, the average temperature would have to be so ludicrously low as to be completely unbelievable for April, thus an absurd figure

Table 19:

.

The average pre-insulation data was obtained over around 2 weeks only in February/March (winter) 2011 then compared to the FULL ANNUAL data (around 52 weeks) AVERAGE from 2012 (7 month average) to 2016. This can only be considered an unrealistic comparison (eg, ‘apples to pears’) and is meaningless.

COMMENT:

The minor average variations in the annual data are the result of the natural variation of internal and external atmospherics. There is no evidence provided, certainly from 2013, to show the insulation has afforded a change.

There is insufficient pre-insulation data to make any proper evaluation of subsequent conditions.

Walls are effectively a constant and will relatively rapidly come into equilibrium with internal/external environments should any changes been made to those walls such as insulation. Such changes do not take years; this is clearly shown in the recorded data

There is no evidence provided that the 2013-2016 data is not simply reflecting the internal/external atmospherics, ie, there has been no real change since 2013 and possibly the year before. In order to do so it would require appropriate statistical analyses (a preliminary statistical evaluation of the data provided does not show any effective change over the years) .

The basic data handling errors and calculations are readily evident and are so fundamentally basic and obvious it is surprising that they were missed in the reported review of the report let alone published. If such erroneous data can readily have escaped review then one must seriously question all previous data recorded from 2011 onwards.
The fact that these fundamental errors are obvious have escaped both the researchers and the reported review does seriously question the experience of both the researchers and reviewers in investigations of this nature, eg, Averages which include ‘no data’, RH of 0%, absurdly low Absolute humidities at levels which are not obtainable as average in the UK.; the report and ‘science’ distinctly shows a lack of scientific rigour.

This report has been published and in the public domain. Therefore, given the errors identified, some at an absurd level and readily evident, then it might be prudent to ‘pull’ the report from further exposure; given the above, this same consideration is likely be relevant to previous reports/data. Based on the evidence in the report there seems to be no justification in continuing to monitor the 3 properties when there is no evidence to show there have been any real changes, certainly since 2013. Therefore given the quality of the report and data such further funding cannot be justified.

G.R.Coleman B.Sc(Hons).,M.R.S.B.,C.Biol.,A.I.M.M.M..

Note from the blog owner:

Following Graham Coleman’s review of the above; Archimetrics Limited (the authors of the report), replied and asked for a pubic response. This can be read on this site here

Speak Your Mind

*

Copyright © 2010 Preservation Expert. Legal Stuff: All the advice and information in the posts on my blog is made in good faith and is based on my experience and knowledge at the time of writing. However, nobody is infallible and whilst I’m confident that most of what I write about preservation issues is accurate, there’s a good chance there’ll be an error or two somewhere. I do change my mind about stuff, as I gain more experience. In view of this you must make your own decisions on whether to follow any advice I write and think about this; I could be wrong. No responsibility will be accepted by the author for any losses anyone may suffer as a result of any mistake or for the consequence of any action you take as a result of reading this blog. If you do suffer a loss, resulting from anything I’ve written, a verbal heartfelt apology will be your only compensation.