def wagen1_plot(c: Config):
"""Plot of wagen1 compared to given specification."""
plt.landscape()
wheel_print = (0.31, 0.25)
wheel_prints = []
for w_i in range(len(truck1.axle_distances) + 1):
if w_i in [1, 2]:
wheel_prints.append([wheel_print, wheel_print])
else:
wheel_prints.append([wheel_print])
plt.subplot(1, 2, 1)
xlim, ylim = topview_vehicle(truck1, wheel_prints=wheel_prints)
plt.title("Truck 1 specification")
plt.xlabel("Width (m)")
plt.ylabel("Length (m)")
plt.subplot(1, 2, 2)
topview_vehicle(truck1, xlim=xlim, ylim=ylim)
plt.title("Truck 1 in simulation")
plt.xlabel("Width (m)")
plt.ylabel("Length (m)")
plt.savefig(c.get_image_path("vehicles", "wagen-1", bridge=False) + ".pdf")
plt.close()
def cracked_concrete_plots(c: Config):
"""Contour plots of cracked concrete scenarios."""
response_type = ResponseType.YTranslation
# 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, 10),
np.linspace(c.bridge.z_min, c.bridge.z_max, 10),
)
]
# Create empty traffic array and collect fem.
response_array = responses_to_traffic_array(
c=c,
traffic_array=load_normal_traffic_array(c)[0],
response_type=response_type,
bridge_scenario=cracked_scenario,
points=points,
sim_runner=OSRunner,
)
for t in range(len(response_array)):
top_view_bridge(c.bridge, abutments=True, piers=True)
responses = Responses.from_responses(
response_type=response_type,
responses=[(response_array[t][p], point)
for p, point in enumerate(points)],
)
plot_contour_deck(c=c, responses=responses, center_norm=True)
plt.title("Cracked Concrete")
plt.savefig(c.get_image_path("cracked-scenario", f"cracked-time-{t}"))
plt.close()
def opensees_default(bridge: Callable[[], Bridge],
os_exe: Optional[str] = None,
**kwargs) -> Config:
"""A Config using OpenSees for a given Bridge.
Args:
bridge: function to return a new Bridge.
os_exe: absolute path to OpenSees binary. Optional, if not given this
will look for OpenSees on the $PATH.
kwargs: keyword arguments passed to the Config constructor.
"""
return Config(
bridge=bridge,
sim_runner=os_runner(os_exe),
vehicle_data_path=os.path.join(project_dir(),
"data/traffic/traffic.csv"),
vehicle_pdf=[
(2.4, 5),
(5.6, 45),
(7.5, 30),
(9, 15),
(11.5, 4),
(12.2, 0.5),
(43, 0),
],
vehicle_pdf_col="length",
**kwargs,
)
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 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 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 vehicle_pdf_groups(c: Config):
"""Return vehicles PDF groups, only ever calculated once."""
if not hasattr(c, "_vehicle_pdf_groups"):
start = timer()
c._vehicle_pdf_groups = _vehicle_pdf_groups(
c.vehicle_data,
c.vehicle_pdf_col,
list(map(lambda x: x[0], c.vehicle_pdf)),
)
print_s(f"Vehicle PDF groups loaded in {timer() - start}")
return c._vehicle_pdf_groups
def pairwise_cluster(c: Config, load: bool):
"""Cluster pairwise maps from healthy and damaged scenarios."""
features_path = c.get_data_path("features", "pairwise-cluster", bridge=False)
if not load:
normal_traffic_array, _ = load_normal_traffic_array(c=c, mins=24)
normal_traffic_array = normal_traffic_array[
int(len(normal_traffic_array) / 24) :
]
response_type = ResponseType.YTranslation
grid_points = [
Point(x=x, y=0, z=-9.65)
for x, _ in itertools.product(
np.linspace(c.bridge.x_min, c.bridge.x_max, 50),
# np.linspace(c.bridge.x_min, c.bridge.x_max, 4),
[1],
)
]
# Collect a list of features per scenarios scenario.
features = []
for damage_scenario in healthy_and_cracked_scenarios[1:]:
damage_c = damage_scenario.use(c)
responses = responses_to_traffic_array(
c=damage_c,
traffic_array=normal_traffic_array,
response_type=response_type,
bridge_scenario=damage_scenario,
points=grid_points,
sim_runner=OSRunner,
).T
ks_values = []
for p0_i, point0 in enumerate(grid_points):
print_i(f"Point {p0_i + 1} / {len(grid_points)}", end="\r")
ks_values.append([])
for p1_i, point1 in enumerate(grid_points):
ks = ks_no_outliers(responses[p0_i], responses[p1_i])
ks_values[-1].append(ks)
features.append((ks_values, damage_scenario.name))
# Save features to disk.
features = np.array(features)
np.save(features_path, features)
features = np.load(features_path)
# Reduce each pairwise map to a sum per sensor.
for f_i, (feature, feature_name) in enumerate(features):
features[f_i] = ([sum(sensor) for sensor in feature], feature_name)
features[f_i] = ([sum(sensor) for sensor in features[f_i]], feature_name)
# Cluster each pairwise map.
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=2)
kmeans.fit(features)
def cover_photo(c: Config, x: float, deformation_amp: float):
"""
TODO: SimParams takes any loads iterable, to be flattened.
TODO: Wrap SimRunner into Config.
TODO: Ignore response type in SimParams (fill in by load_sim_responses).
"""
response_type = ResponseType.YTranslation
sim_responses = load_fem_responses(
c=c,
sim_runner=OSRunner(c),
response_type=response_type,
sim_params=SimParams(
response_types=[response_type],
ploads=list(
chain.from_iterable(
truck1.to_point_loads(
bridge=c.bridge,
time=truck1.time_at(x=x, bridge=c.bridge),
))),
),
)
shells = contour_responses_3d(c=c, sim_responses=sim_responses)
for cmap in [
parula_cmap,
get_cmap("jet"),
get_cmap("coolwarm"),
get_cmap("viridis"),
]:
contour_responses_3d(
c=c,
sim_responses=sim_responses,
deformation_amp=deformation_amp,
shells=shells,
cmap=cmap,
)
plt.axis("off")
plt.grid(False)
plt.savefig(
c.get_image_path(
"cover-photo",
f"cover-photo-deform-{deformation_amp}"
f"-cmap-{cmap.name}.pdf",
))
plt.close()
def traffic_response_plots(c: Config, times: int = 3):
"""Response to normal traffic per scenarios scenario at multiple time steps."""
response_type = ResponseType.YTranslation
# 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, 10),
np.linspace(c.bridge.z_min, c.bridge.z_max, 10),
)
]
# for damage_scenario in all_scenarios(c):
for damage_scenario in [unit_temp_scenario]:
response_array = responses_to_traffic_array(
c=c,
traffic_array=load_normal_traffic_array(c, mins=1)[0],
response_type=response_type,
bridge_scenario=damage_scenario,
points=points,
sim_runner=OSRunner,
)
print(response_array.shape)
mean_response_array = np.mean(response_array, axis=0).T
print(mean_response_array.shape)
print(mean_response_array.shape)
for t in range(times):
time_index = -1 + abs(t)
top_view_bridge(c.bridge, abutments=True, piers=True)
responses = Responses.from_responses(
response_type=response_type,
responses=[(response_array[time_index][p], point)
for p, point in enumerate(points)],
)
plot_contour_deck(c=c,
responses=responses,
center_norm=True,
levels=100)
plt.title(damage_scenario.name)
plt.savefig(
c.get_image_path(
"contour-traffic-response",
f"{damage_scenario.name}-time={time_index}",
))
plt.close()
def load_traffic(
c: Config,
traffic_scenario: TrafficScenario,
max_time: float,
add: Optional[str] = None,
):
"""Load traffic from disk, generated if necessary."""
path = (
c.get_data_path(
"traffic",
_traffic_name(c=c, traffic_scenario=traffic_scenario, max_time=max_time),
acc=False,
)
+ ".npy"
)
print(path)
if add is not None:
path += add
# Create the traffic if it doesn't exist.
if not os.path.exists(path + ".arr"):
traffic_sequence = traffic_scenario.traffic_sequence(
bridge=c.bridge, max_time=max_time
)
traffic = to_traffic(c=c, traffic_sequence=traffic_sequence, max_time=max_time)
traffic_array = to_traffic_array(
c=c, traffic_sequence=traffic_sequence, max_time=max_time
)
with open(path + ".seq", "wb") as f:
dill.dump(traffic_sequence, f)
with open(path + ".tra", "wb") as f:
dill.dump(traffic, f)
with open(path + ".arr", "wb") as f:
np.save(f, traffic_array)
with open(path + ".seq", "rb") as f:
traffic_sequence = dill.load(f)
with open(path + ".tra", "rb") as f:
traffic = dill.load(f)
with open(path + ".arr", "rb") as f:
traffic_array = np.load(f)
return traffic_sequence, traffic, traffic_array
def gradient_pier_displacement_plot(
c: Config,
pier_disp: PierSettlementScenario,
response_type: ResponseType,
title: str,
):
"""Contour plot of piers displaced in an increasing gradient."""
# 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, 10),
np.linspace(c.bridge.z_min, c.bridge.z_max, 10),
)
]
# Create empty traffic array and collect fem.
response_array = responses_to_traffic_array(
c=c,
traffic_array=np.zeros(
(1, len(c.bridge.wheel_tracks(c)) * c.il_num_loads)),
response_type=response_type,
bridge_scenario=pier_disp,
points=points,
fem_runner=OSRunner(c),
)
top_view_bridge(c.bridge, abutments=True, piers=True)
responses = Responses.from_responses(
response_type=response_type,
responses=[(response_array[0][p], point)
for p, point in enumerate(points)],
)
plot_contour_deck(c=c, responses=responses, center_norm=True)
plt.title(title)
plt.savefig(
c.get_image_path("pier-scenarios",
f"pier-displacement-{safe_str(title)}"))
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 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 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 pairwise_sensors(c: Config, dist_measure=ks_no_outliers):
"""Compare distribution of pairs of sensors under HealthyScenario."""
normal_traffic_array, traffic_scenario = load_normal_traffic_array(c)
response_type = ResponseType.YTranslation
points = [
Point(x=x, y=0, z=z)
for x, z in itertools.product(
np.linspace(c.bridge.x_min, c.bridge.x_max, 50),
np.linspace(c.bridge.z_min, c.bridge.z_max, 4),
)
]
bridge_scenario = HealthyScenario()
responses = responses_to_traffic_array(
c=c,
traffic_array=normal_traffic_array,
response_type=response_type,
bridge_scenario=bridge_scenario,
points=points,
sim_runner=OSRunner,
).T
assert len(responses) == len(points)
ks_values_healthy = []
for p0, point0 in enumerate(points):
print_i(f"Point {p0 + 1} / {len(points)}")
ks_values_healthy.append([])
for p1, point1 in enumerate(points):
ks = dist_measure(responses[p0], responses[p1])
ks_values_healthy[-1].append(ks)
plt.landscape()
plt.imshow(ks_values_healthy)
plt.savefig(c.get_image_path("joint-clustering", "healthy-bridge"))
plt.close()
bridge_scenario = each_pier_scenarios(c)[0]
responses = responses_to_traffic_array(
c=c,
traffic_array=normal_traffic_array,
response_type=response_type,
bridge_scenario=bridge_scenario,
points=points,
sim_runner=OSRunner,
).T
assert len(responses) == len(points)
ks_values_damage = []
for p0, point0 in enumerate(points):
print_i(f"Point {p0 + 1} / {len(points)}")
ks_values_damage.append([])
for p1, point1 in enumerate(points):
ks = dist_measure(responses[p0], responses[p1])
ks_values_damage[-1].append(ks)
plt.imshow(ks_values_damage)
plt.savefig(c.get_image_path("joint-clustering", "scenarios-bridge"))
plt.close()
ks_values_comp = []
for p0, point0 in enumerate(points):
ks_values_comp.append([])
for p1, point1 in enumerate(points):
comp = abs(ks_values_healthy[p0][p1] - ks_values_damage[p0][p1])
ks_values_comp[-1].append(comp)
plt.landscape()
plt.imshow(ks_values_comp)
plt.savefig(c.get_image_path("joint-clustering", "scenarios-bridge-comp"))
plt.close()
responses = Responses.from_responses(
response_type=response_type,
responses=[(sum(ks_values_comp[p]), point) for p, point in enumerate(points)],
)
top_view_bridge(c.bridge, abutments=True, piers=True)
plot_contour_deck(c=c, responses=responses)
plt.savefig(c.get_image_path("joint-clustering", "scenarios-bridge-comp-contour"))
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 load_ulm(
c: Config,
response_type: ResponseType,
points: List[Point],
sim_runner: FEMRunner,
):
wheel_zs = c.bridge.wheel_track_zs(c)
filepath = c.get_data_path(
"ulms",
(ULResponses.id_str(
c=c,
response_type=response_type,
sim_runner=sim_runner,
wheel_zs=wheel_zs,
) + str([str(point) for point in points])) + ".ulm",
)
filepath = shorten_path(c=c, bypass_config=True, filepath=filepath)
if os.path.exists(filepath):
with open(filepath, "rb") as f:
return np.load(f)
def ulm_partial(wheel_z):
"""Slice of unit load matrix for one wheel track."""
wheel_track = ULResponses.load_wheel_track(
c=c,
response_type=response_type,
fem_runner=sim_runner,
load_z_frac=c.bridge.z_frac(wheel_z),
run_only=False,
)
partial = np.empty((c.il_num_loads, len(points)))
i = 0
for sim_responses in wheel_track:
for j, point in enumerate(points):
partial[i][j] = sim_responses.at_deck(point, interp=False)
if wheel_z < 0 and i == 302:
log(
c,
f"z = {wheel_z}, i = 302, partial[i][j] = {partial[i][j]}",
)
i += 1
assert i == c.il_num_loads
print_i(f"Calculated unit load matrix for wheel track {wheel_z}")
return partial
# Calculate results in parallel.
print_i(f"Calculating unit load matrix...")
with multiprocessing.Pool(processes=len(wheel_zs)) as pool:
partial_results = pool.map(ulm_partial, wheel_zs)
# And insert into the unit load matrix.
unit_load_matrix = np.empty(
(len(wheel_zs) * c.il_num_loads, len(points)))
for w_ind in range(len(wheel_zs)):
row_ind = w_ind * c.il_num_loads
unit_load_matrix[row_ind:row_ind +
c.il_num_loads] = partial_results[w_ind]
# Divide by unit load, so the value at a cell is the response to 1 kN.
unit_load_matrix /= c.il_unit_load_kn
with open(filepath, "wb") as f:
np.save(f, unit_load_matrix)
return unit_load_matrix
def crack_time_series(
c: Config,
traffic_array,
traffic_array_mins: float,
sensor: Point,
crack_frac: float,
damage,
temps: List[float],
solar: List[float],
):
"""Time series of sensor fem, vertical translation and strain XXB.
Returns a NumPy array of dimensions (2 x len(traffic_array)).
Args:
c: Config, global configuration object.
traffic_array: TrafficArray, traffic flowing over the bridge.
traffic_array_mins: float, minutes of the the traffic flow.
sensor: Point, point at which to collect fem.
crack_frac: float, fraction of time series where crack occurs.
damage: DamageScenario, scenarios that occurs at crack_frac.
temps: List[float], list of air temperature, per temperature minute.
solar: List[float], list of solar radiance, per temperature minute.
"""
assert 0 <= crack_frac <= 1
response_types = [ResponseType.YTranslation, ResponseType.Strain]
half_i = int(len(traffic_array) * crack_frac)
traffic_array_0, traffic_array_1 = traffic_array[:half_i], traffic_array[
half_i:]
assert len(traffic_array_0) + len(traffic_array_1) == len(traffic_array)
half_t = int(len(temps) * crack_frac)
assert len(temps) == len(solar)
# Get the effect of temperature for both response types and damages.
# In each case we have the full days worth of temperature fem.
temp_effect = []
for response_type in response_types:
temp_effect_damages = []
for di, ds in enumerate([HealthyDamage(), damage]):
bots_tops, new_temp_effect = temperature.effect(
c=ds.use(c)[0],
response_type=response_type,
points=[sensor],
# One hour temperature data per minute of traffic data.
len_per_hour=int(len(traffic_array) /
traffic_array_mins) if di == 0 else None,
temps=temps if di == 0 else None,
solar=solar if di == 0 else None,
temps_bt=bots_tops.T[int(len(bots_tops.T) /
2):].T if di == 1 else None,
ret_temps_bt=True,
)
bots_tops = np.array(bots_tops)
temp_effect_damages.append(
new_temp_effect[0] if di ==
1 else new_temp_effect[0][:int(len(new_temp_effect[0]) / 2)])
temp_effect.append(np.concatenate(temp_effect_damages))
print(f"len(temps) = {len(temps)}")
print(f"len_per_hour = {int(len(traffic_array) / traffic_array_mins)}")
print(f"Temperature shape = {temp_effect[-1].shape}")
plt.plot(temp_effect[-1])
plt.savefig(
c.get_image_path("crack",
safe_str(f"save-temps-{response_type}.pdf")))
plt.close()
responses = []
for ri, rt in enumerate(response_types):
responses_healthy_cracked = []
for ds, ta in [(HealthyDamage(), traffic_array_0),
(damage, traffic_array_1)]:
print(
f"Sections in scenarios scenario = {len(ds.use(c)[0].bridge.sections)}"
)
responses_healthy_cracked.append(
responses_to_traffic_array(
c=c,
traffic_array=ta,
response_type=rt,
damage_scenario=ds,
points=[sensor],
).T[0]) # Responses from a single point.
responses.append(np.concatenate(responses_healthy_cracked))
print(f"shape fem without temp = {responses[-1].shape}")
print(f"shape of temp effect = {temp_effect[ri].shape}")
if rt == ResponseType.Strain:
responses[ri] = resize_units("")[0](responses[ri])
responses[ri] += temperature.apply(temp_effect[ri], responses[ri])
responses = np.array(responses)
print(f"Responses shape = {responses.shape}")
return responses
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 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 make_shell_properties_top_view(
c: Config,
shells_name_: str,
prop_name_: str,
refined_: bool,
outline: bool,
lanes: bool,
):
"""Make plots of the shells in top view, coloured by material property."""
original_c = c
# For each scenarios scenario build the model and extract the shells.
for damage_scenario, damage_name in zip(healthy_and_cracked_scenarios,
[None, "cracked"]):
c, sim_params = damage_scenario.use(original_c)
for ctx, ctx_name, refined, in [
(
BuildContext(
add_loads=[Point(x=85, y=0, z=0)],
refinement_radii=[2, 1, 0.5],
),
"refined",
True,
),
(None, "unrefined", False),
]:
if refined != refined_:
continue
bridge_shells = get_bridge_shells(bridge=c.bridge, ctx=ctx)
deck_shells = flatten(bridge_shells[0], Shell)
pier_shells = flatten(bridge_shells[1], Shell)
all_shells = pier_shells + deck_shells
for shells_name, shells in [
("piers", pier_shells),
("deck", deck_shells),
]:
if shells_name != shells_name_:
continue
for prop_name, prop_units, prop_f in [
("Mesh", "", None),
("Thickness", "m", lambda s: np.around(s.thickness, 3)),
("Density", "kg/m", lambda s: np.around(s.density, 3)),
("Poisson's ratio", "m/m", lambda s: s.poissons),
("Young's modulus", "MPa",
lambda s: np.around(s.youngs, 1)),
]:
if prop_name_ not in prop_name.lower():
continue
for cmap in [parula_cmap, default_cmap]:
def top_view():
top_view_bridge(
bridge=c.bridge,
abutments=True,
piers=True,
lanes=lanes,
compass=prop_f is not None,
)
top_view()
shell_properties_top_view(
shells=shells,
prop_f=prop_f,
prop_units=prop_units,
cmap=cmap,
colorbar=prop_f is not None,
# label=prop_f is not None,
outline=outline,
)
top_view()
damage_str = "" if damage_name is None else f" ({damage_name})"
plt.title(
f"{prop_name} of bridge 705's {shells_name}{damage_str}"
)
plt.savefig(
c.get_image_path(
f"geometry/{shells_name}-shells-{ctx_name}-top-view",
safe_str(
f"{prop_name}-{cmap.name}-outline-{outline}-lanes-{lanes}"
) + ".pdf",
))
plt.close()
if prop_f is None:
break
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()