from SW

.idea/SettlementPredictionCode_Step.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.8 (SettlementPrediction)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.9 (settle_prediction)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (SettlementPrediction)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (settle_prediction)" project-jdk-type="Python SDK" />
 </project>

1_S-12.csv

@@ -0,0 +1,144 @@
+Time,Settle,Surcharge
+0,0,3.52
+2,1.2,3.52
+5,3.1,3.52
+8,5.4,3.52
+15,9.6,3.52
+21,21.5,4.835
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1_SP-11.csv

@@ -78,3 +78,48 @@ Time,Settle,Surcharge
 280,356,4.095
 286,358,4.095
 294,360.9,4.095
+300,363,5.256
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+461,418.7,5.256
+468,420,5.256

1_SP-17.csv

@@ -0,0 +1,164 @@
+Time,Settle,Surcharge
+0,0,1.6
+3,11.4,1.6
+7,24.1,1.6
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1_SP-23.csv

@@ -0,0 +1,125 @@
+Time,Settle,Surcharge
+0,0,1.5
+5,6.7,1.5
+8,10.2,1.5
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1_SP-28.csv

@@ -0,0 +1,205 @@
+Time,Surcharge,Settle
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3_SP3-65.csv

@@ -0,0 +1,183 @@
+Time,Settle,Surcharge
+0,0,1.33
+4,7.7,1.33
+8,10.7,1.33
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3_SP3-68.csv

@@ -0,0 +1,128 @@
+Time,Settle,Surcharge
+0,0,1.887
+3,41,1.887
+6,61.5,1.887
+10,73.1,1.887
+13,81.6,1.887
+17,86.9,1.887
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+439,405.6,6.794
+443,408.2,6.794
+446,409.4,6.794

4_S-11.csv

@@ -0,0 +1,158 @@
+Time,Settle,Surcharge
+0,0,3
+9,14.4,3
+14,31.4,3
+24,33.3,3
+27,34.1,3
+28,34.5,3
+29,34.7,3
+31,34.9,3
+34,35.3,3
+36,35.7,3
+38,36,3.5
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settle_prediction_steps.py

@@ -1,3 +1,15 @@
+"""
+Title: Soft ground settlement prediction considering the step loading
+Main Developer: Sang Inn Woo, Ph.D. @ Incheon National University
+Starting Date: 2022-08-11
+Abstract:
+This main objective of this code is to predict
+time vs. (consolidation) settlement of soft clay ground
+under step loading conditions.
+The methodologies used are 1) superposition of time-settlement curves
+and 2) nonlinear regression for hyperbolic curves.
+"""
+
 # =================
 # Import 섹션
 # =================
@@ -5,7 +17,6 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from matplotlib import rcParams
 from scipy.optimize import least_squares
 
 
@@ -26,49 +37,113 @@ def fun_hyper_nonlinear(px, pt, py):
 def fun_hyper_original(px, pt, py):
     return px[0] * pt + px[1] - pt / py
 
+# RMSE 산정
+def fun_rmse(py1, py2):
+    mse = np.square(np.subtract(py1, py2)).mean()
+    return np.sqrt(mse)
 
 
-# =================
+
-# 입력값 설정
+# ================================
-# =================
+# 파일 설정 / Pre-processing (임시)
+# ================================
+
+# 파일명 설정
+#filename = "1_S-12.csv"
+#filename = "1_SP-11.csv"
+#filename = "1_SP-17.csv"
+#filename = "1_SP-23.csv"
+#filename = "3_SP3-65.csv"
+#filename = "3_SP3-68.csv"
+filename = "4_S-11.csv"
+
+# 성토 단계 시작 index 리스트 초기화
+step_start_index = []
+
+# 성토 단계 끝 index + 1 리스트 초기화
+step_end_index = []
+
+# 파일명에 따라서, 성토 단계 index 설정
+if filename == "1_S-12.csv":
+    step_start_index = [0, 56]
+    step_end_index = [56, 143]
+elif filename == "1_SP-11.csv":
+    step_start_index = [0, 10, 37, 79]
+    step_end_index = [10, 37, 79, 124]
+elif filename == "1_SP-17.csv":
+    step_start_index = [0, 122]
+    step_end_index = [122, 163]
+elif filename == "1_SP-23.csv":
+    step_start_index = [0, 18, 40, 90]
+    step_end_index = [18, 40, 90, 124]
+elif filename == "3_SP3-65.csv":
+    step_start_index = [0, 94, 136]
+    step_end_index = [ 94, 136, 182]
+elif filename == "3_SP3-68.csv":
+    step_start_index = [0, 9, 48, 88]
+    step_end_index = [9, 48, 88, 127]
+elif filename == "4_S-11.csv":
+    step_start_index = [0, 10, 46, 51, 120]
+    step_end_index = [10, 46, 51, 120, 157]
+
+# 성토 단계 횟수 파악 및 저장
+num_steps = len(step_start_index)
+
+
+
+# ====================
+# 파일 읽기, 데이터 설정
+# ====================
 
 # CSV 파일 읽기
-data = pd.read_csv("1_SP-11.csv")
+data = pd.read_csv(filename)
 
 # 시간, 침하량, 성토고 배열 생성
 time = data['Time'].to_numpy()
 settle = data['Settle'].to_numpy()
 surcharge = data['Surcharge'].to_numpy()
 
+# 마지막 계측 데이터 index + 1 파악
+final_index = time.size
+
 
 
 # =================
 # 성토 단계 구분
 # =================
 
-# todo: 성토고 데이터를 분석하여, 각 단계 계측 시작일 / 끝일 파악해야함
+# todo: 성토고 데이터를 분석하여, 각 단계 계측 시작 및 끝일에 해당하는 인덱스 파악 필요
 # 꼭 이전 단계 마지막 인덱스와 현재 단계 처음 인덱스가 이어질 필요는 없음
 # (각 단계별 시간, 침하를 초기화 한후 예측을 수행하므로...)
 
 
 
+# ===========================
+# 최종 단계 데이터 사용 범위 조정
+# ===========================
 
-# =================
+# 최종 성토 단계의 데이터 사용 퍼센트 설정 : 사용자 입력값
-# (임시) 입력값 설정
+final_step_predict_percent = 60
-# =================
 
-# 현재 아래 값은 입력값으로 각 데이터로부터 추출해야 함
+# 데이터 사용 퍼센트에 해당하는 기간 계산
-# todo: 단계성토 데이터 14곳에 대해서 아래 정보를 구축해서 정리할 것
+final_step_end_date = time[-1]
-step_start_index = [0, 10, 37]  # 성토 단계 시작 index
+final_step_start_date = time[step_start_index[num_steps - 1]]
-step_end_index = [10, 37, 65]   # 성토 단계 끝 index + 1 --> max. index = 78 따라서 78 + 1 = 79
+final_step_period = final_step_end_date - final_step_start_date
-final_index = time.size         # 마지만 계측 데이터 index + 1
+final_step_predict_end_date = final_step_start_date + final_step_period * final_step_predict_percent / 100
-num_steps = 3                   # 성토 단계 횟수
 
-# todo: 최종 단계에 대해서는 계측 데이터 활용 구간을 조정이 가능해야함
+# 데이터 사용 끝 시점 인덱스 초기화
-# step_end_index의 마지막 값을 조정하여 마지막 성토 구간의 계측 데이터 사용 구간을 조정 가능
+final_step_predict_end_index = -1
-# 본 입력 파일에서는 38 ~ 79 사이의 값을 활용구간에 따라 조정해야 함
 
-# todo: 예측 때 활용하지 않은 계측값에 대해서 RMSE 산정
+# 데이터 사용 끝 시점 인덱스 검색
+count = 0
+for day in time:
+    count = count + 1
+    if day > final_step_predict_end_date:
+        final_step_predict_end_index = count - 1
+        break
+
+# 마지막 성토 단계, 마지막 계측 시점 인덱스 업데이트
+step_end_index[num_steps - 1] = final_step_predict_end_index
 
 
 
@@ -78,11 +153,11 @@ num_steps = 3                   # 성토 단계 횟수
 # =================
 
 # 추가 예측 일 입력
-add_days = 500
+add_days = time[-1]
 
 # 마지막 성토고 및 마지막 계측일 저장
 final_surcharge = surcharge[final_index - 1]
-final_time = time[final_index -1]
+final_time = time[final_index - 1]
 
 # 추가 시간 및 성토고 배열 설정 (100개의 시점 설정)
 time_add = np.linspace(final_time + 1, final_time + add_days, 100)
@@ -102,7 +177,10 @@ final_index = time.size
 # =============================
 
 # 예측 침하량 초기화
-sp = np.zeros(time.size)
+sp_step = np.zeros(time.size)
+
+# 만일 계수 중에 하나가 음수가 나오면 에러 출력
+error_step = 0
 
 # 각 단계별로 진행
 for i in range(0, num_steps):
@@ -112,7 +190,7 @@ for i in range(0, num_steps):
     sm_this_step = settle[step_start_index[i]:step_end_index[i]]
 
     # 이전 단계까지 예측 침하량 중 현재 단계에 해당하는 부분 추출
-    sp_this_step = sp[step_start_index[i]:step_end_index[i]]
+    sp_this_step = sp_step[step_start_index[i]:step_end_index[i]]
 
     # 현재 단계 시작 부터 끝까지 시간 데이터 추출
     tm_to_end = time[step_start_index[i]:final_index]
@@ -142,12 +220,19 @@ for i in range(0, num_steps):
     x_step = res_lsq_hyper_nonlinear.x
     print(x_step)
 
+    # 만일 계수 중에 하나가 음수일 경우, 에러 메세지 출력하고 Break
+    #if x_step[0] < 0 or x_step[0] < 0 :
+    #    print("More than one parameter is negative!")
+    #    error_step = 1
+    #    break
+
     # 현재 단계 예측 침하량 산정 (침하 예측 끝까지)
     sp_to_end_update = generate_data_hyper(x_step, tm_to_end)
 
     # 예측 침하량 업데이트
-    sp[step_start_index[i]:final_index] = \
+    sp_step[step_start_index[i]:final_index] = \
-        sp[step_start_index[i]:final_index] + sp_to_end_update + s0_this_step
+        sp_step[step_start_index[i]:final_index] + sp_to_end_update + s0_this_step
+
 
 
 # =========================================================
@@ -199,6 +284,28 @@ time_hyper = time_hyper + t0_hyper
 
 
 
+# ==========
+# RSME 산정
+# ==========
+
+# RMSE 계산 데이터 구간 설정 (계측, 단계,  비선형 쌍곡선, 기존 쌍곡선)
+sm_rmse = settle[step_start_index[num_steps - 1]:final_step_predict_end_index]
+sp_step_rmse = sp_step[step_start_index[num_steps - 1]:final_step_predict_end_index]
+sp_hyper_nonlinear_rmse = sp_hyper_nonlinear[:final_step_predict_end_index - step_start_index[num_steps - 1]]
+sp_hyper_original_rmse = sp_hyper_original[:final_step_predict_end_index - step_start_index[num_steps - 1]]
+
+# RMSE 산정  (단계, 비선형 쌍곡선, 기존 쌍곡선)
+RMSE_step = fun_rmse(sm_rmse, sp_step_rmse)
+RMSE_hyper_nonlinear = fun_rmse(sm_rmse, sp_hyper_nonlinear_rmse)
+RMSE_hyper_original = fun_rmse(sm_rmse, sp_hyper_original_rmse)
+
+# RMSE 출력 (단계, 비선형 쌍곡선, 기존 쌍곡선)
+print("RMSE(Nonlinear Hyper + Step): %0.3f" %RMSE_step)
+print("RMSE(Nonlinear Hyperbolic): %0.3f" %RMSE_hyper_nonlinear)
+print("RMSE(Original Hyperbolic): %0.3f" %RMSE_hyper_original)
+
+
+
 # =====================
 # Post-Processing
 # =====================
@@ -211,14 +318,14 @@ fig, axes = plt.subplots(2, 1, figsize=(10, 10),
 axes[0].plot(time, surcharge, color='black', label='surcharge height')
 
 # 성토고 그래프 설정
-axes[0].set_ylabel("Surcharge height (m)", fontsize=17)
+axes[0].set_ylabel("Surcharge height (m)", fontsize=15)
 axes[0].set_xlim(left=0)
 axes[0].grid(color="gray", alpha=.5, linestyle='--')
 axes[0].tick_params(direction='in')
 
 # 계측 및 예측 침하량 표시
 axes[1].scatter(time[0:settle.size], -settle, s=50, facecolors='white', edgecolors='black', label='measured data')
-axes[1].plot(time, -sp, linestyle='-', color='blue', label='Nonlinear + Step Loading')
+axes[1].plot(time, -sp_step, linestyle='-', color='blue', label='Nonlinear + Step Loading')
 axes[1].plot(time_hyper, -sp_hyper_nonlinear,
              linestyle='--', color='green', label='Nonlinear Hyperbolic')
 axes[1].plot(time_hyper, -sp_hyper_original,
@@ -236,6 +343,54 @@ axes[1].tick_params(direction='in')
 # 범례 표시
 axes[1].legend(loc=1, ncol=2, frameon=True, fontsize=12)
 
+# 예측 데이터 사용 범위 음영 처리 - 단계성토
+plt.axvspan(0, final_step_predict_end_date,
+            alpha=0.2, color='grey', hatch='///')
+
+# 예측 데이터 사용 범위 음영 처리 - 기존 및 비선형 쌍곡선
+plt.axvspan(final_step_start_date, final_step_predict_end_date,
+            alpha=0.2, color='grey', hatch='///')
+
+# 예측 데이터 사용 범위 표시 화살표 세로 위치 설정
+arrow1_y_loc = 1.3 * min(-settle)
+arrow2_y_loc = 1.4 * min(-settle)
+
+# 예측 데이터 사용 범위 화살표 처리 - 단계성토
+axes[1].arrow(0, arrow1_y_loc, final_step_predict_end_date, 0,
+              head_width=10, color='black', length_includes_head='True')
+axes[1].arrow(final_step_predict_end_date, arrow1_y_loc, -final_step_predict_end_date, 0,
+              head_width=10, color='black', length_includes_head='True')
+
+# 예측 데이터 사용 범위 화살표 처리 - 기존 및 비선형 쌍곡선
+axes[1].arrow(final_step_start_date, arrow2_y_loc,
+              final_step_predict_end_date - final_step_start_date, 0,
+              head_width=10, color='black', length_includes_head='True')
+axes[1].arrow(final_step_predict_end_date, arrow2_y_loc,
+              final_step_start_date - final_step_predict_end_date, 0,
+              head_width=10, color='black', length_includes_head='True')
+
+# Annotation 표시용 공간 설정
+space = max(time) * 0.01
+
+# 예측 데이터 사용 범위 범례 표시 - 단계성토
+plt.annotate('Data Range Used (Nonlinear + Step Loading)', xy=(final_step_predict_end_date, arrow1_y_loc),
+             xytext=(final_step_predict_end_date + space, arrow1_y_loc),
+             horizontalalignment='left', verticalalignment='center')
+
+# 예측 데이터 사용 범위 범례 표시 - 기존 및 비선형 쌍곡선
+plt.annotate('Data Range Used (Nonlinear and Original Hyperbolic)', xy=(final_step_predict_end_date, arrow1_y_loc),
+             xytext=(final_step_predict_end_date + space, arrow2_y_loc),
+             horizontalalignment='left', verticalalignment='center')
+
+# RMSE 출력
+mybox = {'facecolor': 'white', 'edgecolor': 'black', 'boxstyle': 'round', 'alpha': 0.4}
+plt.text(max(time), 0.25 * min(-settle),
+         " RMSE (Nonlinear + Step Loading) = %0.3f" % RMSE_step
+         + "\n" + " RMSE (Nonlinear Hyperbolic) = %0.3f" % RMSE_hyper_nonlinear
+         + "\n" + " RMSE (Original Hyperbolic) = %0.3f" % RMSE_hyper_original,
+         color='r', horizontalalignment='right',
+         verticalalignment='top', fontsize='12', bbox=mybox)
+
 # 그래프 저장
 plt.savefig('output.svg')