Predic'ng  the  corrosion  ini'a'on  'me  of  fresh  concrete  sewers  by  ar'ficial  neural  network  

Guangming Jiang 14 June 2015

Corrosion  processes  &  reac-ons  -­‐  air  &  liquid  phase  

Sulfide oxidation:H2S + 2O2 → H2SO4

Acid attack on cement:CaO·SiO2·2H2O + H2SO4 → CaSO4 + Si(OH)4 + H2OH2SO4 + CaCO3 → CaSO4 + H2CO3

H2SO4 + Ca(OH)2 → CaSO4 ·2H2O (gypsum) 3CaSO4·2H2O + 4CaO·Al2O3·13H2O + 14H2O →(CaO)3·Al2O3·(CaSO4)3·32H2O (ettringite)

Corrosion  processes  &  reac-ons  -­‐  solid  phase  

(Jiang  et  al.  2014)  

Development  of  sewer  corrosion    –  ini-a-on  processes  

•  Surface  neutraliza-on  

o  Weak  acids  from  air  and  

wastewater  (H2S,  CO2,  organic  

acids)  

o  Biologically  generate  sulfuric  acid  

•  Biological  processes    

o  SOB  (from  producing  S0  to  

sulfuric  acid)  

•  Controlling  environmental  factors  

o  H2S  levels;  Temperature;  Rela-ve  humidity  (Joseph  et  al.  2012;  Jiang  et  al.  2015)  

Bilinear  model  of  sewer  corrosion  

•  Many  models  for  the  corrosion  rate  

•  No  model  available  for  the  predic-on  of  tini-a-on   (Wells  and  Melchers,  2014)  

12initiation

servicet Dt

r= +

tservice:  service  life  (year)  tini-a-on:  ini-a-on  -me  (month)  D:  concrete  depth  (mm)  r:  corrosion  rate  (mm/year)  

Laboratory  corrosion  chambers  Factors  

Temperature  (Gas  phase)   16-­‐18oC,  25oC  &  30oC  

Rela-ve    Humidity     100%  &  85-­‐95%  

H2S  level  (ppm)   0,  5,  10,  15,    25  &  50  

3  x  2  x  6  =  36  chambers  

Coupons  Loca-on   Gas-­‐phase  &  par-ally  submerged  

Type   Fresh  concrete  

Exposure   Every  6-­‐9  months  up  to  4.5  years  

2  x  8  =  16  coupons  each  chamber  

Preliminary  analysis   Ini-a-on  -me  tin  Sta-s-cal  analysis  

Controlling  factors  for  tin    

Model  development  

Training  Valida-on  

Field  data  

black-­‐box  model  

Raw  data  

Data  analysis  &  model  development  

Explanatory  analysis  of  tin  

•  GPC:  Temperature  and  RH  are  key  factors,  with  H2S  as  a  secondary.  

•  PSC:  H2S  and  temperature  are  key  factors.  RH  is  not.    

Gas-­‐phase  concrete                                                      Par-ally-­‐submerged  coupons  

ANOVA  of  tin  GPC                                                                    PSC  

H2S  (ppm)   Rela've  humidity  (%)   Temperature  (oC)  

Corrosion  ini'a'

on  'me  (m

onth)  

Mul-ple  linear  regression  model  

296.34 1.68 0.18 0.54 0.84it Location H S RH T= + ∗ − ∗ − ∗ − ∗

Coefficients Es'mate Std.  Error t  value P(>|t|)   Significance  

Intercept 96.34 15.03 6.41 3.49×10-­‐8 ***

Loca'on 1.68 0.77 2.19 3.25×10-­‐2 *

H2S -­‐0.18 0.05 -­‐3.83 3.34×10-­‐4 ***

RH -­‐0.54 0.15 -­‐3.51 9.13×10-­‐4 ***

Temperature -­‐0.84 0.14 -­‐5.83 3.04×10-­‐7 *** •  Reasonably  reflect  the  rela-onship  between  ti  and  controlling  factors  

•  Max  ti  =  96  months  (8  years);  Loca-on  difference  =  3.5  months  

•  R2=0.54  à  nonlinear  rela-onship  

Ar-ficial  Neural  Network  model  

•  ANN  model  design  •  Network  architecture  design  (BP)  

•  Layers  (1  input,  1  hidden,  1  output)  

•  Neurons  (4  input  (1C+3N),  8  hidden,  1  output)  

•  Model  training  with  back-­‐propaga-on  algorithm  •  Data:  Training(70%)/Valida-on(15%)/Tes-ng(15%)  

ANN  model  inspired  by  biological  neuron  systems  generate  the  mathemaCcal  relaConships  between  input  and  output  data  by  idenCfying  the  paFerns  in  data.  -­‐  Data  driven;  black-­‐box;  suitable  for  any  complex  system.  

ANN  model  performance  

ANN platforms and tools: •  Alyuda NeuroIntelligence

•  Matlab

ANN  model  valida-on  

•  Validated  ANN  model  

•  Reasonable  accuracy  

•  Higher  confidence  with  more  data  

Sensi-vity  analysis  &  uncertainty  

•  Sensitivity analysis o  Nonlinear responses to environment

o  Sensitivity order: H2S > Temperature > RH

o  Different sensitivity for different locations

•  Uncertainty (unexplained variances in predictions) o  Constant vs. fluctuating H2S

o  Processes contributing to corrosion

o  Other factors (concrete properties)

Conclusions

•  Artificial neural network (ANN) performs well in predicting tin based on sewer environmental conditions.

•  The ANN model can be used to improve the understanding of corrosion mechanisms.

•  The ANN model is a good base framework for further expansion by including more corrosion data.

Acknowledgements

•  Queensland Government Accelerate fellowship

•  ARC Linkage Project LP0882016, the Sewer Corrosion and Odour Research (SCORe) Project