def make_boundary_plot(c: Config):
"""Top view of bridge with boundary conditions."""
plt.landscape()
top_view_bridge(c.bridge, abutments=True, piers=True, compass=False)
plt.vlines(
[0, c.bridge.length],
c.bridge.z_min,
c.bridge.z_max,
lw=5,
color="orange",
label=" Y = 1, Z = 1",
)
for p_i, pier in enumerate(c.bridge.supports):
z_min_top, z_max_top = pier.z_min_max_bottom()
x_min, x_max = pier.x_min_max_top()
x_center = x_min + ((x_max - x_min) / 2)
plt.vlines(
[x_center],
z_min_top,
z_max_top,
lw=5,
color="red" if (8 <= p_i <= 15) else "orange",
label="X = 1, Y = 1, Z = 1" if p_i == 8 else None,
)
legend_marker_size(plt.legend(), 50)
plt.title("Bridge 705 boundary conditions of nodal supports")
plt.tight_layout()
plt.savefig(c.get_image_path("sensors", "boundary.pdf"))
plt.close()
def make_available_sensors_plot(c: Config, pier_radius: float,
track_radius: float, edge_radius: float):
"""Scatter plot of sensors used for classification."""
top_view_bridge(c.bridge, abutments=True, piers=True, compass=False)
plot_deck_sensors(
c=c,
without=without.points(
c=c,
pier_radius=pier_radius,
track_radius=track_radius,
edge_radius=edge_radius,
),
label=True,
)
for l_i, load in enumerate([Point(x=21, z=-8.4), Point(x=33, z=-4)]):
plt.scatter(
[load.x],
[load.z],
color="red",
marker="o",
s=50,
label="Sensor of interest" if l_i == 0 else None,
)
legend_marker_size(plt.legend(), 50)
plt.title(f"Sensors available for classification on Bridge 705")
plt.tight_layout()
plt.savefig(c.get_image_path("sensors", "unavailable-sensors.pdf"))
plt.close()
def temperature_effect_date(c: Config, month: str, vert: bool):
temp = __init__.load(name=month)
point = Point(x=51, y=0, z=-8.4)
plt.landscape()
def plot_hours():
if not vert:
return
label_set = False
for dt in temp["datetime"]:
if np.isclose(float(dt.hour + dt.minute), 0):
label = None
if not label_set:
label = "Time at vertical line = 00:00"
label_set = True
plt.axvline(x=dt, linewidth=1, color="black", label=label)
# Plot the temperature.
plt.subplot(2, 1, 1)
plot_hours()
plt.scatter(
temp["datetime"],
temp["temp"],
c=temp["missing"],
cmap=mpl.cm.get_cmap("bwr"),
s=1,
)
plt.ylabel("Temperature (°C)")
plt.xlabel("Date")
plt.gcf().autofmt_xdate()
plt.title(f"Temperature in {str(month[0]).upper()}{month[1:]}")
plt.legend()
# Plot the effect at a point.
response_type = ResponseType.YTranslation
plt.subplot(2, 1, 2)
plot_hours()
effect = __init__.effect(
c=c, response_type=response_type, points=[point], temps=temp["temp"]
)[0]
plt.scatter(
temp["datetime"],
effect * 1000,
c=temp["missing"],
cmap=mpl.cm.get_cmap("bwr"),
s=1,
)
plt.ylabel(f"{response_type.name()} (mm)")
plt.xlabel("Date")
plt.gcf().autofmt_xdate()
plt.title(f"{response_type.name()} to unit thermal loading in {month}")
# Save.
plt.tight_layout()
plt.savefig(c.get_image_path("classify/temperature", f"{month}.png"))
plt.savefig(c.get_image_path("classify/temperature", f"{month}.pdf"))
plt.close()
def number_of_uls_plot(c: Config):
"""Plot error as a function of number of unit load simulations."""
if not c.shorten_paths:
raise ValueError("This plot requires --shorten-paths true")
response_type = ResponseType.YTranslation
num_ulss = np.arange(100, 2000, 10)
chosen_uls = 600
point = Point(x=c.bridge.x_max - (c.bridge.length / 2), y=0, z=-8.4)
wagen1_time = truck1.time_at(x=point.x, bridge=c.bridge)
print_i(f"Wagen 1 time at x = {point.x:.3f} is t = {wagen1_time:.3f}")
# Determine the reference value.
truck_loads = flatten(
truck1.to_point_load_pw(time=wagen1_time, bridge=c.bridge), PointLoad)
print_i(f"Truck loads = {truck_loads}")
sim_responses = load_fem_responses(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
sim_params=SimParams(ploads=truck_loads,
response_types=[response_type]),
)
ref_value = sim_responses.at_deck(point, interp=True) * 1000
print_i(f"Reference value = {ref_value}")
# Collect the data.
total_load = []
num_loads = []
responses = []
for num_uls in num_ulss:
c.il_num_loads = num_uls
# Nested in here because it depends on the setting of 'il_num_loads'.
truck_loads = flatten(
truck1.to_wheel_track_loads(c=c, time=wagen1_time), PointLoad)
num_loads.append(len(truck_loads))
total_load.append(sum(map(lambda l: l.kn, truck_loads)))
sim_responses = load_fem_responses(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
sim_params=SimParams(ploads=truck_loads,
response_types=[response_type]),
)
responses.append(sim_responses.at_deck(point, interp=True) * 1000)
# Plot the raw fem, then error on the second axis.
plt.landscape()
# plt.plot(num_ulss, fem)
# plt.ylabel(f"{response_type.name().lower()} (mm)")
plt.xlabel("ULS")
error = np.abs(np.array(responses) - ref_value).flatten() * 100
# ax2 = plt.twinx()
plt.plot(num_ulss, error)
plt.ylabel("Error (%)")
plt.title(
f"Error in {response_type.name()} to Truck 1 as a function of ULS")
# Plot the chosen number of ULS.
chosen_error = np.interp([chosen_uls], num_ulss, error)[0]
plt.axhline(
chosen_error,
label=f"At {chosen_uls} ULS, error = {np.around(chosen_error, 2)} %",
color="black",
)
plt.axhline(0,
color="red",
label="Response from direct simulation (no wheel tracks)")
plt.legend()
plt.tight_layout()
plt.savefig(c.get_image_path("paramselection", "uls.pdf"))
plt.close()
# Additional verification plots.
plt.plot(num_ulss, total_load)
plt.savefig(c.get_image_path("paramselection",
"uls-verify-total-load.pdf"))
plt.close()
plt.plot(num_ulss, num_loads)
plt.savefig(c.get_image_path("paramselection", "uls-verify-num-loads.pdf"))
plt.close()
def experiment_noise(c: Config):
"""Plot displacement and strain noise from dynamic test 1"""
################
# Displacement #
################
plt.portrait()
# Find points of each sensor.
displa_labels = ["U13", "U26", "U29"]
displa_points = []
for displa_label in displa_labels:
sensor_x, sensor_z = _displa_sensor_xz(displa_label)
displa_points.append(Point(x=sensor_x, y=0, z=sensor_z))
# For each sensor plot and estimate noise.
side = 700
for s_i, displa_label in enumerate(displa_labels):
# First plot the signal, and smoothed signal.
plt.subplot(len(displa_points), 2, (s_i * 2) + 1)
with open(f"validation/experiment/D1a-{displa_label}.txt") as f:
data = list(map(float, f.readlines()))
# Find the center of the plot, minimum point in first 15000 points.
data_center = 0
for i in range(15000):
if data[i] < data[data_center]:
data_center = i
data = data[data_center - side:data_center + side]
smooth = savgol_filter(data, 31, 3)
plt.plot(data, linewidth=1)
plt.plot(smooth, linewidth=1)
plt.ylim(-0.8, 0.3)
plt.title(f"{displa_label} in dynamic test")
# Then plot subtraction of smoothed from noisey.
plt.subplot(len(displa_points), 2, (s_i * 2) + 2)
noise = data - smooth
plt.plot(noise, label=f"σ = {np.around(np.std(noise), 4)}")
plt.legend()
plt.title(f"Noise from {displa_label}")
plt.tight_layout()
plt.savefig(c.get_image_path("params", "noise-displa.pdf"))
plt.close()
##########
# Strain #
##########
plt.portrait()
# Find points of each sensor.
strain_labels = ["T1", "T10", "T11"]
strain_points = []
for strain_label in strain_labels:
sensor_x, sensor_z = _strain_sensor_xz(strain_label)
strain_points.append(Point(x=sensor_x, y=0, z=sensor_z))
# For each sensor plot and estimate noise.
side = 700
xmin, xmax = np.inf, -np.inf
for s_i, strain_label in enumerate(strain_labels):
# First plot the signal, and smoothed signal.
plt.subplot(len(strain_points), 2, (s_i * 2) + 1)
with open(f"validation/experiment/D1a-{strain_label}.txt") as f:
data = list(map(float, f.readlines()))
# Find the center of the plot, minimum point in first 15000 points.
data_center = 0
for i in range(15000):
if data[i] < data[data_center]:
data_center = i
data = data[data_center - side:data_center + side]
smooth = savgol_filter(data, 31, 3)
plt.plot(data, linewidth=1)
plt.plot(smooth, linewidth=1)
plt.title(f"{strain_label} in dynamic test")
# Then plot subtraction of smoothed from noisey.
plt.subplot(len(strain_points), 2, (s_i * 2) + 2)
noise = data - smooth
plt.plot(noise, label=f"σ = {np.around(np.std(noise), 4)}")
plt.legend()
plt.title(f"Noise from {strain_label}")
plt.tight_layout()
plt.savefig(c.get_image_path("params", "noise-strain.pdf"))
plt.close()
def events(c: Config, x: float, z: float):
"""Plot events due to normal traffic."""
point = Point(x=x, y=0, z=z)
# 10 seconds of 'normal' traffic.
max_time = 10
traffic_scenario = normal_traffic(c=c, lam=5, min_d=2)
# Create the 'TrafficSequence' and 'TrafficArray'.
traffic_sequence = traffic_scenario.traffic_sequence(
bridge=c.bridge, max_time=max_time
)
traffic_array = to_traffic_array(
c=c, traffic_sequence=traffic_sequence, max_time=max_time
)
# Find when the simulation has warmed up, and when 'TrafficArray' begins.
warmed_up_at = traffic_sequence[0][0].time_left_bridge(c.bridge)
traffic_array_starts = (int(warmed_up_at / c.sensor_hz) + 1) * c.sensor_hz
print(f"warmed up at = {warmed_up_at}")
print(f"traffic_array_starts = {traffic_array_starts}")
traffic_array_ends = traffic_array_starts + (len(traffic_array) * c.sensor_hz)
print(f"traffic_array_ends = {traffic_array_ends}")
point_lane_ind = c.bridge.closest_lane(z)
vehicles = list(set(ts[0] for ts in traffic_sequence))
print(len(vehicles))
print(vehicles[0])
vehicles = sorted(
set(ts[0] for ts in traffic_sequence if ts[0].lane == point_lane_ind),
key=lambda v: -v.init_x_frac,
)
print(len(vehicles))
print(vehicles[0])
event_indices = []
vehicle_times = [v.time_at(x=x - 2, bridge=c.bridge) for v in vehicles]
for v, t in zip(vehicles, vehicle_times):
print(f"Vehicle {v.init_x_frac} {v.mps} at time {t}")
start_time = int(t / c.sensor_hz) * c.sensor_hz
print(f"start_time = {start_time}")
ta_start_time = np.around(start_time - traffic_array_starts, 8)
print(f"ta start time = {ta_start_time}")
ta_start_index = int(ta_start_time / c.sensor_hz)
print(f"ta start index = {ta_start_index}")
ta_end_index = ta_start_index + int(c.event_time_s / c.sensor_hz)
print(f"ta end index = {ta_end_index}")
if ta_start_index >= 0 and ta_end_index < len(traffic_array):
event_indices.append((ta_start_index, ta_end_index))
print(event_indices)
responses = (
responses_to_traffic_array(
c=c,
traffic_array=traffic_array,
response_type=ResponseType.YTranslation,
damage_scenario=healthy_scenario,
points=[point],
sim_runner=OSRunner(c),
)
* 1000
)
# fem = add_displa_noise(fem)
print(responses.shape)
plt.portrait()
for event_ind, (event_start, event_end) in enumerate(event_indices):
plt.subplot(len(event_indices), 1, event_ind + 1)
plt.plot(responses[event_start : event_end + 1])
plt.tight_layout()
plt.savefig(c.get_image_path("classify/events", "events.pdf"))
plt.close()
def plot_mmm_strain_convergence(
c: Config,
pier: int,
df: pd.DataFrame,
all_strains: Dict[float, Responses],
title: str,
without: Optional[Callable[[Point], bool]] = None,
append: Optional[str] = None,
):
"""Plot convergence of given fem as model size grows."""
# A grid of points 1m apart, over which to calculate fem.
grid = [
Point(x=x, y=0, z=z)
for x, z in itertools.product(
np.linspace(c.bridge.x_min, c.bridge.x_max, int(c.bridge.length)),
np.linspace(c.bridge.z_min, c.bridge.z_max, int(c.bridge.width)),
)
]
# If requested, remove some values from the fem.
if without is not None:
grid = [point for point in grid if not without(point)]
for msl, strains in all_strains.items():
print(f"Removing points from strains with max_shell_len = {msl}")
all_strains[msl] = strains.without(without)
# Collect fem over all fem, and over the grid. Iterate by
# decreasing max_shell_len.
mins, maxes, means = [], [], []
gmins, gmaxes, gmeans = [], [], []
max_shell_lens = []
for msl, strains in sorted(all_strains.items(), key=lambda kv: -kv[0]):
max_shell_lens.append(msl)
print_i(f"Gathering strains with max_shell_len = {msl}", end="\r")
grid_strains = np.array([strains.at_deck(point, interp=True) for point in grid])
gmins.append(scalar(np.min(grid_strains)))
gmaxes.append(scalar(np.max(grid_strains)))
gmeans.append(scalar(np.mean(grid_strains)))
strains = np.array(list(strains.values()))
mins.append(scalar(np.min(strains)))
maxes.append(scalar(np.max(strains)))
means.append(scalar(np.mean(strains)))
print()
# Normalize and plot the mins, maxes, and means.
def normalize(ys):
print(ys)
return ys / np.mean(ys[-5:])
mins, maxes, means = normalize(mins), normalize(maxes), normalize(means)
gmins, gmaxes, gmeans = normalize(gmins), normalize(gmaxes), normalize(gmeans)
deck_nodes = [df.at[msl, "deck-nodes"] for msl in max_shell_lens]
pier_nodes = [df.at[msl, "pier-nodes"] for msl in max_shell_lens]
num_nodes = np.array(deck_nodes) + np.array(pier_nodes)
print(f"MSLs = {max_shell_lens}")
print(f"num_nodes = {num_nodes}")
# Plot all lines, for debugging.
plt.landscape()
plt.plot(num_nodes, mins, label="mins")
plt.plot(num_nodes, maxes, label="maxes")
plt.plot(num_nodes, means, label="means")
plt.plot(num_nodes, gmins, label="gmins")
plt.plot(num_nodes, gmaxes, label="gmaxes")
plt.plot(num_nodes, gmeans, label="gmeans")
plt.grid(axis="y")
plt.xlabel("Nodes in FEM")
plt.ylabel("Strain")
plt.title(title)
plt.tight_layout()
plt.legend()
plt.savefig(
c.get_image_path("convergence-pier-strain", f"mmm-{append}-all.pdf", acc=False)
)
plt.close()
# Only plot some lines, for the thesis.
plt.landscape()
plt.plot(num_nodes, gmins, label="Minimum")
plt.plot(num_nodes, gmaxes, label="Maximum")
plt.plot(num_nodes, gmeans, label="Mean")
plt.grid(axis="y")
plt.title(title)
plt.xlabel("Nodes in FEM")
plt.ylabel("Strain")
plt.legend()
plt.tight_layout()
plt.savefig(
c.get_image_path("convergence-pier-strain", f"mmm-{append}.pdf", acc=False)
)
plt.close()
def plot_nesw_convergence(
c: Config,
df: pd.DataFrame,
responses: Dict[float, Responses],
point: Point,
max_distance: float,
from_: str,
):
"""Plot convergence of strain at different points around a load."""
delta_distance = 0.05
skip = 3
# Create color mappable for distances.
norm = matplotlib.colors.Normalize(vmin=0, vmax=max_distance)
cmap = matplotlib.cm.get_cmap("jet")
mappable = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap)
color = lambda d: mappable.to_rgba(d)
# For each compass point.
compass_dir = {
"N": (0, 1),
"E": (1, 0),
"S": (0, -1),
"W": (-1, 0),
}
plt.square()
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax, compass, compass_name, in zip(
axes.flat, ["N", "S", "E", "W"], ["North", "South", "East", "West"]
):
# Collect data into fem.
x_mul, z_mul = compass_dir[compass]
for distance in np.arange(0, max_distance, step=delta_distance)[::skip]:
dist_point = Point(
x=point.x + (distance * x_mul),
y=point.y,
z=point.z + (distance * z_mul),
)
print(dist_point)
if (
dist_point.x < c.bridge.x_min
or dist_point.x > c.bridge.x_max
or dist_point.z < c.bridge.z_min
or dist_point.z > c.bridge.z_max
):
break
line_responses = []
for max_shell_len, sim_responses in responses.items():
deck_nodes = float(df.at[max_shell_len, "deck-nodes"])
pier_nodes = float(df.at[max_shell_len, "pier-nodes"])
line_responses.append(
(
deck_nodes + pier_nodes,
# max_shell_len,
scalar(sim_responses.at_deck(dist_point, interp=True)),
)
)
line_responses = np.array(sorted(line_responses, key=lambda t: t[0])).T
ax.plot(line_responses[0], line_responses[1], color=color(distance))
if distance > max_distance:
break
ax.grid(axis="y")
ax.set_title(
f"Strain at increasing distance\nin direction {compass_name} from\n{from_}"
)
ax.set_xlabel("Nodes in FEM")
ax.set_ylabel("Strain")
# ax.set_xlim(ax.get_xlim()[1], ax.get_xlim()[0])
plt.tight_layout()
clb = plt.colorbar(mappable, ax=axes.ravel())
clb.ax.set_title("Distance (m)")
def comparison_plots_705(c: Config, run_only: bool, scatter: bool):
"""Make contour plots for all verification points on bridge 705."""
# from classify.scenario.bridge import transverse_crack
# c = transverse_crack().use(c)[0]
positions = [
# (52, -8.4, "a"),
(34.95459, 26.24579 - 16.6, "a"),
(51.25051, 16.6 - 16.6, "b"),
(89.98269, 9.445789 - 16.6, "c"),
(102.5037, 6.954211 - 16.6, "d"),
# (34.95459, 29.22606 - 16.6, "a"),
# (51.25051, 16.6 - 16.6, "b"),
# (92.40638, 12.405 - 16.6, "c"),
# (101.7649, 3.973938 - 16.6, "d"),
]
diana_values = pd.read_csv("validation/diana-screenshots/min-max.csv")
response_types = [ResponseType.YTranslation, ResponseType.Strain]
# For each response type and loading position first create contour plots for
# OpenSees. Then finally create subplots comparing to Diana.
cmap = diana_cmap_r
for load_x, load_z, label in positions:
for response_type in response_types:
# Setup the metadata.
if response_type == ResponseType.YTranslation:
rt_str = "displa"
unit_str = "mm"
elif response_type == ResponseType.Strain:
rt_str = "strain"
unit_str = "E-6"
else:
raise ValueError("Unsupported response type")
row = diana_values[diana_values["name"] == f"{label}-{rt_str}"]
dmin, dmax = float(row["dmin"]), float(row["dmax"])
omin, omax = float(row["omin"]), float(row["omax"])
amin, amax = max(dmin, omin), min(dmax, omax)
levels = np.linspace(amin, amax, 16)
# Create the OpenSees plot.
loads = [
PointLoad(
x_frac=c.bridge.x_frac(load_x),
z_frac=c.bridge.z_frac(load_z),
kn=100,
)
]
fem_responses = load_fem_responses(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
sim_params=SimParams(ploads=loads,
response_types=response_types),
)
if run_only:
continue
title = (
f"{response_type.name()} from a {loads[0].kn} kN point load at"
+ f"\nx = {load_x:.3f}m, z = {load_z:.3f}m, with ")
save = lambda prefix: c.get_image_path(
"validation/diana-comp",
safe_str(f"{prefix}{response_type.name()}") + ".pdf",
)
top_view_bridge(c.bridge, piers=True, abutments=True)
fem_responses = fem_responses.resize()
sci_format = response_type == ResponseType.Strain
plot_contour_deck(
c=c,
responses=fem_responses,
ploads=loads,
cmap=cmap,
levels=levels,
sci_format=sci_format,
decimals=4,
scatter=scatter,
)
plt.title(title + "OpenSees")
plt.tight_layout()
plt.savefig(save(f"{label}-"))
plt.close()
# Finally create label/title the Diana plot.
if label is not None:
# First plot and clear, just to have the same colorbar.
plot_contour_deck(c=c,
responses=fem_responses,
ploads=loads,
cmap=cmap,
levels=levels)
plt.cla()
# Then plot the bridge and
top_view_bridge(c.bridge, piers=True, abutments=True)
plt.imshow(
mpimg.imread(
f"validation/diana-screenshots/{label}-{rt_str}.png"),
extent=(
c.bridge.x_min,
c.bridge.x_max,
c.bridge.z_min,
c.bridge.z_max,
),
)
dmin_s = f"{dmin:.4e}" if sci_format else f"{dmin:.4f}"
dmax_s = f"{dmax:.4e}" if sci_format else f"{dmax:.4f}"
dabs_s = (f"{abs(dmin - dmax):.4e}"
if sci_format else f"{abs(dmin - dmax):.4f}")
for point, leg_label, color, alpha in [
((load_x, load_z), f"{loads[0].kn} kN load", "r", 1),
((0, 0), f"min = {dmin_s} {fem_responses.units}", "r", 0),
((0, 0), f"max = {dmax_s} {fem_responses.units}", "r", 0),
((0, 0), f"|min-max| = {dabs_s} {fem_responses.units}",
"r", 0),
]:
plt.scatter(
[point[0]],
[point[1]],
label=leg_label,
marker="o",
color=color,
alpha=alpha,
)
plt.legend()
plt.title(title + "Diana")
plt.xlabel("X position (m)")
plt.ylabel("Z position (m)")
plt.tight_layout()
plt.savefig(save(f"{label}-diana-"))
plt.close()
def piers_displaced(c: Config):
"""Contour plots of pier displacement for the given pier indices."""
pier_indices = [4, 5]
response_types = [ResponseType.YTranslation, ResponseType.Strain]
axis_values = pd.read_csv("validation/axis-screenshots/piers-min-max.csv")
for r_i, response_type in enumerate(response_types):
for p in pier_indices:
# Run the simulation and collect fem.
sim_responses = load_fem_responses(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
sim_params=SimParams(displacement_ctrl=PierSettlement(
displacement=c.pd_unit_disp, pier=p), ),
)
# In the case of stress we map from kn/m2 to kn/mm2 (E-6) and then
# divide by 1000, so (E-9).
assert c.pd_unit_disp == 1
if response_type == ResponseType.Strain:
sim_responses.to_stress(c.bridge).map(lambda r: r * 1e-9)
# Get min and max values for both Axis and OpenSees.
rt_str = ("displa" if response_type == ResponseType.YTranslation
else "stress")
row = axis_values[axis_values["name"] == f"{p}-{rt_str}"]
dmin, dmax = float(row["dmin"]), float(row["dmax"])
omin, omax = float(row["omin"]), float(row["omax"])
amin, amax = max(dmin, omin), min(dmax, omax)
levels = np.linspace(amin, amax, 16)
# Plot and save the image. If plotting strains use Axis values for
# colour normalization.
# norm = None
from plot import axis_cmap_r
cmap = axis_cmap_r
top_view_bridge(c.bridge, abutments=True, piers=True)
plot_contour_deck(c=c,
cmap=cmap,
responses=sim_responses,
levels=levels)
plt.tight_layout()
plt.title(
f"{sim_responses.response_type.name()} from 1mm pier settlement with OpenSees"
)
plt.savefig(
c.get_image_path(
"validation/pier-displacement",
safe_str(f"pier-{p}-{sim_responses.response_type.name()}")
+ ".pdf",
))
plt.close()
# First plot and clear, just to have the same colorbar.
plot_contour_deck(c=c,
responses=sim_responses,
cmap=cmap,
levels=levels)
plt.cla()
# Save the axis plots.
axis_img = mpimg.imread(
f"validation/axis-screenshots/{p}-{rt_str}.png")
top_view_bridge(c.bridge, abutments=True)
plt.imshow(
axis_img,
extent=(
c.bridge.x_min,
c.bridge.x_max,
c.bridge.z_min,
c.bridge.z_max,
),
)
# Plot the load and min, max values.
for point, leg_label, color in [
((0, 0), f"min = {np.around(dmin, 3)} {sim_responses.units}",
"r"),
((0, 0), f"max = {np.around(dmax, 3)} {sim_responses.units}",
"r"),
(
(0, 0),
f"|min-max| = {np.around(abs(dmax - dmin), 3)} {sim_responses.units}",
"r",
),
]:
plt.scatter(
[point[0]],
[point[1]],
label=leg_label,
marker="o",
color=color,
alpha=0,
)
if response_type == ResponseType.YTranslation:
plt.legend()
# Title and save.
plt.title(
f"{response_type.name()} from 1mm pier settlement with AxisVM")
plt.xlabel("X position (m)")
plt.ylabel("Z position (m)")
plt.tight_layout()
plt.savefig(
c.get_image_path(
"validation/pier-displacement",
f"{p}-axis-{rt_str}.pdf",
))
plt.close()
def point_load_response_plots(c: Config,
x: float,
z: float,
kn: int = 1000,
run: bool = False):
"""Response to a point load per scenarios scenario."""
response_types = [ResponseType.YTranslation, ResponseType.Strain]
# scenarios = all_scenarios(c)
damage_scenarios = [HealthyScenario(), transverse_crack()]
# 10 x 10 grid of points on the bridge deck where to record fem.
points = [
Point(x=x, y=0, z=z) for x, z in itertools.product(
np.linspace(c.bridge.x_min, c.bridge.x_max, 30),
np.linspace(c.bridge.z_min, c.bridge.z_max, 100),
)
]
for response_type in response_types:
all_responses = []
for damage_scenario in damage_scenarios:
sim_params = SimParams(
response_types=[response_type],
ploads=[
PointLoad(x_frac=c.bridge.x_frac(x),
z_frac=c.bridge.z_frac(z),
kn=kn)
],
)
use_c, sim_params = damage_scenario.use(c=c, sim_params=sim_params)
all_responses.append(
load_fem_responses(
c=use_c,
sim_params=sim_params,
response_type=response_type,
sim_runner=OSRunner(use_c),
run=run,
).resize())
amin, amax = np.inf, -np.inf
for sim_responses in all_responses:
responses = np.array(list(sim_responses.values()))
amin = min(amin, min(responses))
amax = max(amax, max(responses))
for d, damage_scenario in enumerate(damage_scenarios):
top_view_bridge(c.bridge, abutments=True, piers=True)
plot_contour_deck(
c=c,
responses=all_responses[d],
levels=100,
norm=colors.Normalize(vmin=amin, vmax=amax),
decimals=10,
)
plt.title(damage_scenario.name)
plt.tight_layout()
plt.savefig(
c.get_image_path(
"contour/point-load",
safe_str(
f"x-{x:.2f}-z-{z:.2f}-kn-{kn}-{response_type.name()}-{damage_scenario.name}"
) + ".pdf",
))
plt.close()
def time_series_plot(c: Config, n: float):
"""Plot 24min time series of cracking, for multiple cracked bridges.
For each bridge (hard-coded), a time series of strain fem is plotted.
For each bridge it is initially in healthy condition, and the crack occurs
halfway through.
Args:
n: float, meters in front of the crack zone where to place sensor.
"""
# First construct one day (24 minutes) of traffic.
total_mins = 24
total_seconds = total_mins * 60
traffic_scenario = normal_traffic(c=c, lam=5, min_d=2)
traffic_sequence, traffic, traffic_array = load_traffic(
c=c,
traffic_scenario=traffic_scenario,
max_time=total_seconds,
)
traffic_array.shape
# Temperatures for one day.
temps_day = temperature.from_to_mins(
temperature.load("holly-springs"),
datetime.fromisoformat(f"2019-07-03T00:00"),
datetime.fromisoformat(f"2019-07-03T23:59"),
)
print(f"len temps = {len(temps_day['solar'])}")
print(f"len temps = {len(temps_day['temp'])}")
# Then generate some cracking time series.
damages = [
HealthyDamage(),
transverse_crack(),
transverse_crack(length=14.0, at_x=48.0),
]
sensors = [
Point(x=52, z=-8.4), # Sensor in middle of lane.
Point(x=damages[1].crack_area(c.bridge)[0] - n,
z=-8.4), # Sensor in front of crack zone.
Point(x=damages[2].crack_area(c.bridge)[0] - n,
z=-8.4), # Sensor in front of crack zone.
]
[print(f"Sensor {i} = {sensors[i]}") for i in range(len(sensors))]
time_series = [
crack_time_series(
c=c,
traffic_array=traffic_array,
traffic_array_mins=total_mins,
sensor=sensor,
crack_frac=0.5,
damage=damage,
temps=temps_day["temp"],
solar=temps_day["solar"],
) for damage, sensor in zip(damages, sensors)
]
plt.portrait()
for i, (y_trans, strain) in enumerate(time_series):
x = np.arange(len(strain)) * c.sensor_hz / 60
x_m = sensors[i].x
damage_str = "Healthy Bridge"
if i == 1:
damage_str = "0.5 m crack zone"
if i == 2:
damage_str = "14 m crack zone"
plt.subplot(len(time_series), 2, i * 2 + 1)
plt.plot(x, y_trans * 1000, color="tab:blue")
if i < len(time_series) - 1:
plt.tick_params(axis="x", bottom=False, labelbottom=False)
else:
plt.xlabel("Hours")
plt.title(f"At x = {x_m} m\n{damage_str}")
plt.ylabel("Y trans. (mm)")
plt.subplot(len(time_series), 2, i * 2 + 2)
plt.plot(x, strain * 1e6, color="tab:orange")
if i < len(time_series) - 1:
plt.tick_params(axis="x", bottom=False, labelbottom=False)
else:
plt.xlabel("Hours")
plt.title(f"At x = {x_m} m,\n{damage_str}")
plt.ylabel("Microstrain XXB")
plt.tight_layout()
plt.savefig(c.get_image_path("crack", "time-series-q5.pdf"))
plt.close()
