From 7cb8c9e85c76c8bf4c47efec055a1692069e891d Mon Sep 17 00:00:00 2001
From: Pedro Arduino <parduino@uw.edu>
Date: Mon, 10 Jun 2024 16:53:34 -0700
Subject: [PATCH]  small change case1 6

---
 source/.case_1.rst.swp | Bin 40960 -> 40960 bytes
 source/.case_1.rst.un~ | Bin 234155 -> 235866 bytes
 source/case_1.rst      |   4 +++-
 source/case_1.rst~     |  22 +++++++++++++++++-----
 4 files changed, 20 insertions(+), 6 deletions(-)

diff --git a/source/.case_1.rst.swp b/source/.case_1.rst.swp
index 40c4169bc316636f1db33301ead726e291a88cd2..640fd82bb637feba22034fc471879f848228e815 100644
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diff --git a/source/.case_1.rst.un~ b/source/.case_1.rst.un~
index 86a24783030ca62bc9774e28e98ac8ec04328912..07a81153a820379db7fe19629b284d51b43a1adf 100644
GIT binary patch
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diff --git a/source/case_1.rst b/source/case_1.rst
index 6d3004f..dc47874 100644
--- a/source/case_1.rst
+++ b/source/case_1.rst
@@ -30,6 +30,8 @@ The example problems in this project will utilize the scenario, soil profile, an
      Fig. 1. Problem statement.
     
 
+
+
 .. list-table:: Soil Profile Parameters
    :widths: 25 25 50
    :header-rows: 1
@@ -330,7 +332,7 @@ The figure shows Cc and Precon pressure are the most relevant parameters.
 A more in-depth analysis using prior and posterior distributions reveals that the posterior distributions from the Bayesian calibration process result in more accurate and less uncertain settlement estimations. The figure below illustrates these distributions.
 
 .. figure:: ./images/case1_calibration_PriorPost.png
-          :scale: 80%
+          :scale: 70%
           :align: center
 
           Fig. 10. Prior and posterior distributions from Bayesian calibration.
diff --git a/source/case_1.rst~ b/source/case_1.rst~
index eb9da5a..a300176 100644
--- a/source/case_1.rst~
+++ b/source/case_1.rst~
@@ -26,7 +26,6 @@ The example problems in this project will utilize the scenario, soil profile, an
 .. figure:: ./images/case1_settlementProblem.png
     :scale: 45 %
     :align: center
-    :figclass: align-center>
 
      Fig. 1. Problem statement.
     
@@ -231,6 +230,10 @@ Example One - Forward Propagation
 The results for Forward Propagation are outlined below:
 
 .. figure:: ./images/case1_ForwardPropagationResults.png
+            :align: center
+
+            Fig. 4. Forward propagation results.
+
 
 The results indicate that, given the mean parameters and standard deviation, a total settlement of 1.31 inches is expected with a standard deviation of 0.88 inches (CoV = 0.66). The corresponding histogram, based on Latin Hypercube Sampling (LHS), along with the associated normal distribution curve, is shown in the figure below:
 
@@ -238,7 +241,7 @@ The results indicate that, given the mean parameters and standard deviation, a t
    :scale: 40%
    :align: center
 
-   Fig. 1. QuoFEM propagation histogram.
+   Fig. 5. QuoFEM propagation histogram.
 
 
 Example Two - Sensitivity Analysis
@@ -262,12 +265,14 @@ The results for the Sensitivity Analysis in QuoFEM are outlined below. Uncertain
 .. figure:: ./images/case1_Sensitivity2.png
     :scale: 60 %
     :align: center
-    :figclass: align-center>
+
+    Fig. 6. QuoFEM sensitivity results.
 
 .. figure:: ./images/case1_Sensitivity.png
     :scale: 100 %
     :align: center
-    :figclass: align-center>
+
+    Fig. 7. QuoFEM interface.
 
 
 Example Three - Parameter Calibration
@@ -287,6 +292,8 @@ When testing the two different deterministic calibration algorithms supported in
             :scale: 80%
             :align: center
 
+            Fig. 8. Settlement field as a function of Cc and Precon pressure.
+
 Bayesian Calibration
 ^^^^^^^^^^^^^^^^^^^^
 
@@ -314,15 +321,20 @@ The results for Bayesian Calibration are outlined below:
 .. figure:: ./images/case1_BayesianResults1.png
 
 .. figure:: ./images/case1_BayesianResults2.png
+            :align: center
+
+            Fig. 9. QuoFEM Bayesian calibration results.
 
 The figure shows Cc and Precon pressure are the most relevant parameters. 
 
 A more in-depth analysis using prior and posterior distributions reveals that the posterior distributions from the Bayesian calibration process result in more accurate and less uncertain settlement estimations. The figure below illustrates these distributions.
 
 .. figure:: ./images/case1_calibration_PriorPost.png
-          :scale: 80%
+          :scale: 70%
           :align: center
 
+          Fig. 10. Prior and posterior distributions from Bayesian calibration.
+
 
 Remarks
 -------