def make_node_plots(original_c: Config):
"""Make all variations of 3d scatter plots of nodes."""
for damage_scenario in healthy_and_cracked_scenarios:
c, sim_params = damage_scenario.use(original_c, SimParams([]))
for ctx, ctx_name in [
(BuildContext(add_loads=[Point(x=85, y=0, z=0)]), "refined"),
(None, "unrefined"),
]:
bridge_nodes = get_bridge_nodes(bridge=c.bridge, ctx=ctx)
deck_nodes = set(flatten(bridge_nodes[0], Node))
pier_nodes = set(flatten(bridge_nodes[1], Node))
all_nodes = set(flatten(bridge_nodes, Node))
# For each combination of parameters plot the nodes.
for nodes_name, nodes in [
("all", all_nodes),
("deck", deck_nodes),
("pier", pier_nodes),
]:
node_scatter_3d(nodes=nodes)
plt.title(f"Nodes of {c.bridge.name}")
plt.savefig(
c.get_image_path(
f"geometry/nodes-{ctx_name}",
safe_str(f"{nodes_name}") + ".pdf",
))
plt.close()
def test_to_wheel_track_loads():
# As Truck 1 enters the bridge, total load is less than Truck 1.
enter_times = np.linspace(entering_time, entered_time - 0.001, 100)
for time in enter_times:
loads = truck1.to_wheel_track_loads(c=c, time=time)
flat_loads = flatten(loads, PointLoad)
total_kn = sum(map(lambda l: l.load, flat_loads))
assert total_kn < truck1.total_kn()
# As Truck 1 is fully on the bridge, total load equals that of Truck 1.
on_times = np.linspace(entered_time, leaving_time, 100)
truck_front_x = np.arange(8, 102)
more_times = np.array(
[truck1.time_at(x=x, bridge=c.bridge) for x in truck_front_x])
on_times = np.concatenate((on_times, more_times))
for time in on_times:
loads = truck1.to_wheel_track_loads(c=c, time=time)
flat_loads = flatten(loads, PointLoad)
total_kn = sum(map(lambda l: l.load, flat_loads))
assert np.isclose(total_kn, truck1.total_kn())
# As Truck 1 is leaving the bridge, total load is less than Truck 1.
leave_times = np.linspace(leaving_time + 0.001, left_time, 100)
for time in leave_times:
loads = truck1.to_wheel_track_loads(c=c, time=time)
flat_loads = flatten(loads, PointLoad)
total_kn = sum(map(lambda l: l.load, flat_loads))
assert total_kn < truck1.total_kn()
def test_loads_to_traffic_array():
# First with one point load per wheel.
wagen1_loads = [
flatten(truck1.to_point_load_pw(time=time, bridge=c.bridge), PointLoad)
for time in np.linspace(entered_time, leaving_time, 1000)
]
for row in loads_to_traffic_array(c=c, loads=wagen1_loads):
assert sum(row) == truck1.total_kn()
# Then with point loads based on wheel tracks.
wagen1_loads = [
flatten(truck1.to_wheel_track_loads(c=c, time=time), PointLoad)
for time in np.linspace(entered_time, leaving_time, 1000)
]
for row in loads_to_traffic_array(c=c, loads=wagen1_loads):
assert np.isclose(sum(row), truck1.total_kn())
def to_point_load_pw(
self, time: float, bridge: Bridge, list: bool = False
) -> Union[List[Tuple[PointLoad, PointLoad]], List[PointLoad]]:
"""A tuple of point load per axle, one point load per wheel."""
z0, z1 = self.wheel_tracks_zs(bridge=bridge, meters=True)
assert z0 < z1
kn_per_axle = self.kn_per_axle()
result = []
# For each axle.
for x_i, x in enumerate(self.xs_at(time=time, bridge=bridge)):
# Skip axle if not on the bridge.
if (x < bridge.x_min and not np.isclose(x, bridge.x_min)) or (
x > bridge.x_max and not np.isclose(x, bridge.x_max)
):
continue
# Two wheel load intensities.
kn_wheel = kn_per_axle[x_i] / 2
result.append(
(
PointLoad(x=x, z=z0, load=kn_wheel),
PointLoad(x=x, z=z1, load=kn_wheel),
)
)
if list:
return flatten(result, PointLoad)
return result
def compare_load_positions(c: Config):
"""Compare load positions (normal vs. buckets)."""
c.il_num_loads = 10
num_times = 1000
# Wagen 1 from the experimental campaign.
point = Point(x=c.bridge.x_max / 2, y=0, z=-8.4)
end_time = uni_axle_vehicle.time_left_bridge(bridge=c.bridge)
vehicle_times = list(np.linspace(0, end_time, num_times))
plt.portrait()
pw_loads = [
flatten(uni_axle_vehicle.to_point_load_pw(time=time, bridge=c.bridge),
PointLoad) for time in vehicle_times
]
pw_load_xs = [[c.bridge.x(l.x_frac) for l in pw_loads[time_ind]]
for time_ind in range(len(pw_loads))]
plt.subplot(3, 1, 1)
# for l in pw_load_xs:
# print(l)
plt.plot([l[0] for l in pw_load_xs])
plt.plot([l[1] for l in pw_load_xs])
wt_loads = [
flatten(uni_axle_vehicle.to_wheel_track_loads(c=c, time=time),
PointLoad) for time in vehicle_times
]
wt_load_xs = [[c.bridge.x(l.x_frac) for l in wt_loads[time_ind]]
for time_ind in range(len(wt_loads))]
plt.subplot(3, 1, 2)
plt.scatter(vehicle_times, [l[0] for l in wt_load_xs], label="0")
plt.scatter(vehicle_times, [l[1] for l in wt_load_xs], label="1")
plt.legend()
wt_load_kn = [[l.kn for l in wt_loads[time_ind]]
for time_ind in range(len(wt_loads))]
plt.subplot(3, 1, 3)
for l in wt_load_kn:
print(l)
plt.scatter(vehicle_times, [l[0] for l in wt_load_kn], label="0")
plt.scatter(vehicle_times, [l[1] for l in wt_load_kn], label="1")
plt.legend()
plt.tight_layout()
plt.savefig(c.get_image_path("verification", "compare-load-positions.pdf"))
plt.close()
def make_shell_properties_3d(original_c: Config):
"""Make plots of the shells in 3D, coloured by material property."""
# For each scenarios scenario build the model and extract the shells.
for damage_scenario in healthy_and_cracked_scenarios:
c, sim_params = damage_scenario.use(original_c, SimParams([]))
for ctx, ctx_name in [
(BuildContext(add_loads=[Point(x=85, y=0, z=0)]), "refined"),
(None, "unrefined"),
]:
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 = flatten(bridge_shells, Shell)
# For each combination of parameters plot the shells.
for shells_name, shells in [
("pier", pier_shells),
("all", all_shells),
("deck", deck_shells),
]:
for outline, label in itertools.product([True, False],
[True, False]):
for prop_name, prop_units, prop_f in [
("Thickness", "m", lambda s: s.thickness),
("Density", "kg/m", lambda s: s.density),
("Poisson's ratio", "m/m", lambda s: s.poissons),
("Young's modulus", "MPa", lambda s: s.youngs),
]:
for cmap in [default_cmap, get_cmap("tab10")]:
shell_properties_3d(
shells=shells,
prop_units=prop_units,
prop_f=prop_f,
cmap=cmap,
outline=outline,
label=label,
colorbar=not label,
)
plt.title(f"{prop_name} of {c.bridge.name}")
plt.savefig(
c.get_image_path(
f"geometry/shells-{ctx_name}-3d",
safe_str(
f"{shells_name}-{prop_name}-outline-{outline}-{cmap.name}"
) + ".pdf",
))
plt.close()
def test_to_point_load_pw():
# As Truck 1 enters the bridge.
wagen1_times = np.linspace(entering_time, entered_time - 0.001, 100)
for time in wagen1_times:
loads = truck1.to_point_load_pw(time=time, bridge=c.bridge)
flat_loads = flatten(loads, PointLoad)
total_kn = sum(map(lambda l: l.load, flat_loads))
assert total_kn < truck1.total_kn()
# As Truck 1 is fully on the bridge.
wagen1_times = np.linspace(entered_time, leaving_time, 100)
for time in wagen1_times:
loads = truck1.to_point_load_pw(time=time, bridge=c.bridge)
flat_loads = flatten(loads, PointLoad)
total_kn = sum(map(lambda l: l.load, flat_loads))
assert total_kn == truck1.total_kn()
# As Truck 1 is leaving the bridge.
wagen1_times = np.linspace(leaving_time + 0.001, left_time, 100)
for time in wagen1_times:
loads = truck1.to_point_load_pw(time=time, bridge=c.bridge)
flat_loads = flatten(loads, PointLoad)
total_kn = sum(map(lambda l: l.load, flat_loads))
assert total_kn < truck1.total_kn()
def to_wheel_track_loads(
self, c: "Config", time: float, flat: bool = False
) -> List[Tuple[List[PointLoad], List[PointLoad]]]:
z0, z1 = self.wheel_tracks_zs(bridge=c.bridge, meters=True)
assert z0 < z1
result = []
for axle_loads in self.to_wheel_track_loads_(c=c, time=time):
left, right = [], []
left_loads, right_loads = axle_loads
for load_x, load_kn in left_loads:
left.append(PointLoad(x=load_x, z=z0, load=load_kn))
for load_x, load_kn in right_loads:
right.append(PointLoad(x=load_x, z=z1, load=load_kn))
result.append((left, right))
if flat:
return flatten(result, PointLoad)
return result
def to_wheel_track_loads_(
self,
c: "Config",
time: float,
flat: bool = False,
wheel_track_xs: Optional[List[float]] = None,
):
"""Load intensities and positions per axle, per wheel.
"Bucketed" to fit onto wheel tracks.
NOTE: In each tuple of two point loads, one tuple per wheel, each point
load is for a unit load position in the wheel track. Each point load is
weighted by the distance to the unit load.
"""
if wheel_track_xs is None:
wheel_track_xs = c.bridge.wheel_track_xs(c)
xs = self.xs_at(time=time, bridge=c.bridge)
kns = self.kn_per_axle()
result = []
assert len(xs) == len(kns)
# For each axle.
for x, kn in zip(xs, kns):
# Skip axle if not on the bridge.
if (x < c.bridge.x_min and not np.isclose(x, c.bridge.x_min)) or (
x > c.bridge.x_max and not np.isclose(x, c.bridge.x_max)
):
continue
left, right = [], []
for (load_x, load_frac) in self.to_wheel_track_xs(
c=c, wheel_x=x, wheel_track_xs=wheel_track_xs,
):
if load_frac > 0:
bucket_kn = kn / 2 * load_frac
left.append((load_x, bucket_kn))
right.append((load_x, bucket_kn))
result.append((left, right))
if flat:
return flatten(result, PointLoad)
return result
def responses_to_vehicles_d(
c: Config,
response_type: ResponseType,
points: List[Point],
mv_vehicles: List[Vehicle],
times: List[float],
binned: bool = True,
damage_scenario: Scenario = HealthyScenario(),
):
"""Response vehicles via direct simulation (not using superposition).
"""
if not isinstance(damage_scenario, HealthyScenario):
raise ValueError("Only HealthyDamage supported in direct simulation")
if binned:
loads = [[v.to_wheel_track_loads(c=c, time=time) for v in mv_vehicles]
for time in times]
else:
print_w(f"Not using fractions of wheel track bins in simulation")
loads = [[
v.to_point_load_pw(time=time, bridge=c.bridge) for v in mv_vehicles
] for time in times]
loads = [flatten(vehicle_loads, PointLoad) for vehicle_loads in loads]
print([len(load_) for load_ in loads])
print(loads[0])
print(loads[-1])
assert isinstance(loads, list)
assert isinstance(loads[0], list)
assert isinstance(loads[0][0], PointLoad)
return responses_to_loads_d(
c=c,
response_type=response_type,
points=points,
loads=loads,
damage_scenario=damage_scenario,
)
def top_view_plot(c: Config, max_time: int, skip: int, damage_scenario):
response_type = ResponseType.YTranslation
# Create the traffic.
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=max_time,
)
assert len(traffic) == traffic_array.shape[0]
# Points on the deck to collect fem.
deck_points = [
Point(x=x, y=0, z=z) for x in np.linspace(
c.bridge.x_min, c.bridge.x_max, num=int(c.bridge.length * 2))
for z in np.linspace(
c.bridge.z_min, c.bridge.z_max, num=int(c.bridge.width * 2))
# for x in np.linspace(c.bridge.x_min, c.bridge.x_max, num=30)
# for z in np.linspace(c.bridge.z_min, c.bridge.z_max, num=10)
]
point = Point(x=21, y=0, z=-8.4) # Point to plot
deck_points.append(point)
# Traffic array to fem array.
responses_array = responses_to_traffic_array(
c=c,
traffic_array=traffic_array,
damage_scenario=damage_scenario,
points=deck_points,
response_type=response_type,
)
# Temperature effect July 1st.
temps_2019 = temperature.load("holly-springs")
temps_2019["temp"] = temperature.resize(temps_2019["temp"])
effect_2019 = temperature.effect(
c=c,
response_type=response_type,
points=deck_points,
temps=temps_2019["temp"],
solar=temps_2019["solar"],
len_per_hour=60,
).T
# The effect is ordered by time series and then by points. (104910, 301)
assert len(effect_2019) == len(temps_2019)
july_2019_i, july_2019_j = temperature.from_to_indices(
temps_2019,
datetime.fromisoformat(f"2019-10-01T00:00"),
datetime.fromisoformat(f"2019-10-01T23:59"),
)
temp_effect = []
for i in range(len(deck_points)):
temp_effect.append(
temperature.apply(
# Effect for July 1st, for the current point..
effect=effect_2019.T[i][july_2019_i:july_2019_j],
# ..for the length of the time series.
responses=responses_array,
))
temp_effect = np.array(temp_effect)
plt.subplot(2, 1, 1)
plt.plot(effect_2019.T[-1])
plt.subplot(2, 1, 2)
plt.plot(temp_effect[-1])
plt.show()
# Determine response due to pier settlement.
pd_response_at_point = 0
if isinstance(damage_scenario, PierDispDamage):
pd_expt = list(
DCMatrix.load(c=c,
response_type=response_type,
fem_runner=OSRunner(c)))
for pier_displacement in damage_scenario.pier_disps:
pd_sim_responses = pd_expt[pier_displacement.pier]
pd_response_at_point += pd_sim_responses.at_deck(
point, interp=False) * (pier_displacement.displacement /
c.pd_unit_disp)
# Resize fem if applicable to response type.
resize_f, units = resize_units(response_type.units())
if resize_f is not None:
responses_array = resize_f(responses_array)
temp_effect = resize_f(temp_effect.T).T
print(np.mean(temp_effect[-1]))
pd_response_at_point = resize_f(pd_response_at_point)
responses_w_temp = responses_array + temp_effect.T
# Determine levels of the colourbar.
amin, amax = np.amin(responses_array), np.amax(responses_array)
# amin, amax = min(amin, -amax), max(-amin, amax)
levels = np.linspace(amin, amax, 25)
# All vehicles, for colour reference.
all_vehicles = flatten(traffic, Vehicle)
# Iterate through each time index and plot results.
warmed_up_at = traffic_sequence[0][0].time_left_bridge(c.bridge)
# Plot for each time step.
for t_ind in range(len(responses_array))[::skip]:
plt.landscape()
# Plot the bridge top view.
plt.subplot2grid((3, 1), (0, 0), rowspan=2)
top_view_bridge(c.bridge, compass=False, lane_fill=False, piers=True)
top_view_vehicles(
bridge=c.bridge,
mv_vehicles=flatten(traffic[t_ind], Vehicle),
time=warmed_up_at + t_ind * c.sensor_hz,
all_vehicles=all_vehicles,
)
responses = Responses(
response_type=response_type,
responses=[(responses_array[t_ind][p_ind], deck_points[p_ind])
for p_ind in range(len(deck_points))],
units=units,
)
plot_contour_deck(c=c,
responses=responses,
levels=levels,
mm_legend=False)
plt.scatter(
[point.x],
[point.z],
label=f"Sensor in bottom plot",
marker="o",
color="red",
zorder=10,
)
plt.legend(loc="upper right")
plt.title(
f"{response_type.name()} after {np.around(t_ind * c.sensor_hz, 4)} seconds"
)
# Plot the fem at a point.
plt.subplot2grid((3, 1), (2, 0))
time = t_ind * c.sensor_hz
plt.axvline(x=time,
color="black",
label=f"Current time = {np.around(time, 4)} s")
plt.plot(
np.arange(len(responses_array)) * c.sensor_hz,
responses_w_temp.T[-1],
color="red",
label="Total effect",
)
if isinstance(damage_scenario, PierDispDamage):
plt.plot(
np.arange(len(responses_array)) * c.sensor_hz,
np.ones(temp_effect[-1].shape) * pd_response_at_point,
color="green",
label="Pier settlement effect",
)
plt.plot(
np.arange(len(responses_array)) * c.sensor_hz,
temp_effect[-1],
color="blue",
label="Temperature effect",
)
plt.ylabel(f"{response_type.name()} ({responses.units})")
plt.xlabel("Time (s)")
plt.title(f"{response_type.name()} at sensor in top plot")
plt.legend(loc="upper right", framealpha=1)
# Finally save the image.
name = f"{damage_scenario.name}-{response_type.name()}-{t_ind}"
plt.tight_layout()
plt.savefig(c.get_image_path("classify/top-view", f"{name}.pdf"))
plt.savefig(c.get_image_path("classify/top-view/png", f"{name}.png"))
plt.close()
def compare_responses(c: Config):
"""Compare fem to Truck 1, direct simulation and matmul."""
assert c.il_num_loads == 600
num_times = 50
close_times = 200
# Running time:
# responses_to_vehicles_d: num_times * 8
# responses_to_vehicles_d: 4 * il_num_loads
# responses_to_loads_m: 0 (4 * il_num_loads)
# responses_to_loads_m: 0 (4 * il_num_loads)
# Wagen 1 from the experimental campaign.
point = Point(x=c.bridge.x_max - (c.bridge.length / 2), y=0, z=-8.4)
end_time = wagen1.time_left_bridge(bridge=c.bridge)
wagen1_times = list(np.linspace(0, end_time, num_times))
more_wagen1_times = list(
np.linspace(
wagen1.time_at(x=point.x - 2, bridge=c.bridge),
wagen1.time_at(x=point.x + 2, bridge=c.bridge),
close_times,
))
wagen1_times = sorted(wagen1_times + more_wagen1_times)
plt.portrait()
# Start with fem from direct simulation.
responses_not_binned = responses_to_vehicles_d(
c=c,
response_type=ResponseType.YTranslation,
points=[point],
mv_vehicles=[wagen1],
times=wagen1_times,
sim_runner=OSRunner(c),
binned=False,
)
plt.subplot(4, 1, 1)
plt.title(f"{len(wagen1_times)} fem")
plt.plot(wagen1_times, responses_not_binned)
# Then fem from direct simulation with binning.
c.shorten_paths = True
responses_binned = responses_to_vehicles_d(
c=c,
response_type=ResponseType.YTranslation,
points=[point],
mv_vehicles=[wagen1],
times=wagen1_times,
sim_runner=OSRunner(c),
binned=True,
)
c.shorten_paths = False
plt.subplot(4, 1, 2)
plt.title(f"{len(wagen1_times)} fem (binned)")
plt.plot(wagen1_times, responses_binned)
xlim = plt.xlim()
num_times = int(end_time / c.sensor_hz)
wagen1_times = np.linspace(0, end_time, num_times)
# Then from 'TrafficArray' we get fem, without binning.
wagen1_loads = [
flatten(wagen1.to_point_load_pw(time=time, bridge=c.bridge), PointLoad)
for time in wagen1_times
]
responses_ulm = responses_to_traffic_array(
c=c,
traffic_array=loads_to_traffic_array(c=c, loads=wagen1_loads),
response_type=ResponseType.YTranslation,
damage_scenario=healthy_scenario,
points=[point],
sim_runner=OSRunner(c),
)
plt.subplot(4, 1, 3)
plt.title(f"{num_times} fem with ULS = {c.il_num_loads} traffic_array")
plt.plot(wagen1_times, np.array(responses_ulm).reshape(-1, 1))
plt.xlim(xlim)
# # Then from 'TrafficArray' we get fem, with binning.
wagen1_loads = [
flatten(wagen1.to_wheel_track_loads(c=c, time=time), PointLoad)
for time in wagen1_times
]
responses_ulm_binned = responses_to_traffic_array(
c=c,
traffic_array=loads_to_traffic_array(c=c, loads=wagen1_loads),
response_type=ResponseType.YTranslation,
damage_scenario=healthy_scenario,
points=[point],
sim_runner=OSRunner(c),
)
plt.subplot(4, 1, 4)
plt.title(
f"{num_times} fem from {c.il_num_loads} il_num_loads\ntraffic_array binned"
)
plt.plot(wagen1_times, np.array(responses_ulm_binned).reshape(-1, 1))
plt.xlim(xlim)
plt.tight_layout()
plt.savefig(
c.get_image_path("system-verification", "compare-time-series.pdf"))
def compare_axles(c: Config):
"""Compare fem between uniaxle vehicles and Truck 1."""
assert c.il_num_loads == 600
point = Point(x=c.bridge.x_max / 2, y=0, z=-8.4)
end_time = wagen1.time_left_bridge(bridge=c.bridge)
num_times = int(end_time / c.sensor_hz)
wagen1_times = np.linspace(0, end_time, num_times)
plt.portrait()
wagen1_loads = [
flatten(wagen1.to_wheel_track_loads(c=c, time=time), PointLoad)
for time in wagen1_times
]
responses_ulm = responses_to_traffic_array(
c=c,
traffic_array=loads_to_traffic_array(c=c, loads=wagen1_loads),
response_type=ResponseType.YTranslation,
damage_scenario=healthy_scenario,
points=[point],
sim_runner=OSRunner(c),
)
plt.subplot(3, 1, 1)
plt.title(
f"{num_times} fem with ULS = {c.il_num_loads} (Wagen 1 (4 axles))")
plt.plot(wagen1_times, np.array(responses_ulm).reshape(-1, 1))
bi_axle_loads = [
flatten(bi_axle_vehicle.to_wheel_track_loads(c=c, time=time),
PointLoad) for time in wagen1_times
]
responses_ulm = responses_to_traffic_array(
c=c,
traffic_array=loads_to_traffic_array(c=c, loads=bi_axle_loads),
response_type=ResponseType.YTranslation,
damage_scenario=healthy_scenario,
points=[point],
sim_runner=OSRunner(c),
)
plt.subplot(3, 1, 2)
plt.title(f"{num_times} fem with ULS = {c.il_num_loads} (2 axles)")
plt.plot(wagen1_times, np.array(responses_ulm).reshape(-1, 1))
uni_axle_loads = [
flatten(uni_axle_vehicle.to_wheel_track_loads(c=c, time=time),
PointLoad) for time in wagen1_times
]
responses_ulm = responses_to_traffic_array(
c=c,
traffic_array=loads_to_traffic_array(c=c, loads=uni_axle_loads),
response_type=ResponseType.YTranslation,
damage_scenario=healthy_scenario,
points=[point],
sim_runner=OSRunner(c),
)
plt.subplot(3, 1, 3)
plt.title(f"{num_times} fem with ULS = {c.il_num_loads} (1 axle)")
plt.plot(wagen1_times, np.array(responses_ulm).reshape(-1, 1))
plt.tight_layout()
plt.savefig(c.get_image_path("system-verification", "compare-axles.pdf"))
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 sum_loads(loads):
"""Sum of the load intensity (kn) of all given loads."""
return sum(map(lambda l: l.load, flatten(loads, PointLoad)))
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 truck_1_time_series(c: Config):
"""Time series of 3 sensors to Truck 1's movement."""
side_s = 7
side = int(side_s * (1 / c.sensor_hz))
assert wagen1.x_at(time=0, bridge=c.bridge) == 0
# Get times and loads for Truck 1.
end_time = wagen1.time_left_bridge(c.bridge)
wagen1_times = np.linspace(-end_time, end_time * 2,
int((end_time * 3) / c.sensor_hz))
print_i("Calculating Truck 1 loads")
wagen1_loads = [
flatten(wagen1.to_wheel_track_loads(c=c, time=time), PointLoad)
# flatten(wagen1.to_point_load_pw(time=time, bridge=c.bridge), PointLoad)
for time in wagen1_times
]
print_i("Calculated Truck 1 loads")
def set_labels(ylabel: str, xlabel: str):
for i, y, x in [
(1, True, False),
(2, False, False),
(3, True, False),
(4, False, False),
(5, True, True),
(6, False, True),
]:
plt.subplot(3, 2, i)
ax = plt.gca()
if y:
plt.ylabel(ylabel)
else:
ax.axes.yaxis.set_ticklabels([])
if x:
plt.xlabel(xlabel)
ax.axes.xaxis.set_ticklabels([])
ymin, ymax = np.inf, -np.inf
for i in range(1, 6 + 1):
plt.subplot(3, 2, i)
ymin = min(ymin, plt.ylim()[0])
ymax = max(ymax, plt.ylim()[1])
for i in range(1, 6 + 1):
plt.subplot(3, 2, i)
plt.ylim((ymin, ymax))
################
# Vert. trans. #
################
plt.portrait()
# Find points of each sensor.
displa_labels = ["U13", "U26", "U29"]
displa_points = [
Point(x=sensor_x, y=0, z=sensor_z) for sensor_x, sensor_z in
[displa_sensor_xz(displa_label) for displa_label in displa_labels]
]
# Ensure points and truck are on the same lane.
assert all(p.z < 0 for p in displa_points)
# Results from simulation.
responses_truck1 = responses_to_traffic_array(
c=c,
traffic_array=loads_to_traffic_array(c=c, loads=wagen1_loads),
response_type=ResponseType.YTranslation,
damage_scenario=healthy_scenario,
points=displa_points,
).T
for s_i, sensor_responses in enumerate(responses_truck1):
plt.subplot(len(displa_points), 2, (s_i * 2) + 1)
# Find the center of the plot, minimum point in the data.
data_center = 0
for i in range(len(sensor_responses)):
if sensor_responses[i] < sensor_responses[data_center]:
data_center = i
# sensor_responses = add_displa_noise(sensor_responses) * 1000
sensor_responses = sensor_responses * 1000
plt.plot(sensor_responses[data_center - side:data_center + side])
plt.title(f"{displa_labels[s_i]} in simulation")
# Results from experiment.
side = int(side / ((1 / c.sensor_hz) / 100))
print_i(f"{side} points each side of center")
for s_i, displa_label in enumerate(displa_labels):
plt.subplot(len(displa_points), 2, (s_i * 2) + 2)
with open(f"validation/experiment/D1a-{displa_label}.txt") as f:
data = list(map(float, f.readlines()))
side_expt = int(side_s * (len(data) / 240))
# 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
plt.plot(data[data_center - side_expt:data_center + side_expt])
plt.title(f"{displa_label} in dynamic test")
set_labels("Y translation (mm)", "Time")
plt.tight_layout()
plt.savefig(
c.get_image_path("validation/truck-1", "time-series-vert-trans.pdf"))
plt.close()
##########
# Strain #
##########
plt.portrait()
# Find points of each sensor.
strain_labels = ["T1", "T10", "T11"]
strain_points = [
Point(x=sensor_x, y=0, z=sensor_z) for sensor_x, sensor_z in
[strain_sensor_xz(strain_label) for strain_label in strain_labels]
]
# Results from simulation.
responses_truck1 = responses_to_traffic_array(
c=c,
traffic_array=loads_to_traffic_array(c=c, loads=wagen1_loads),
response_type=ResponseType.Strain,
damage_scenario=healthy_scenario,
points=strain_points,
).T
for s_i, sensor_responses in enumerate(responses_truck1):
plt.subplot(len(strain_points), 2, (s_i * 2) + 1)
# Find the center of the plot, minimum point in the data.
data_center = 0
for i in range(len(sensor_responses)):
if sensor_responses[i] > sensor_responses[data_center]:
data_center = i
# sensor_responses = add_strain_noise(sensor_responses)
# plt.plot(sensor_responses)
plt.plot(sensor_responses[data_center - side:data_center + side])
# plt.plot(sensor_responses[data_center - side : data_center + side])
plt.title(f"{strain_labels[s_i]} in simulation")
# Results from experiment.
print_i(f"{side} points each side of center")
for s_i, strain_label in enumerate(strain_labels):
plt.subplot(len(strain_points), 2, (s_i * 2) + 2)
with open(f"validation/experiment/D1a-{strain_label}.txt") as f:
data = list(map(float, f.readlines()))
side_expt = int(side_s * (len(data) / 240))
# 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
# plt.plot(data)
plt.plot(data[data_center - side_expt:data_center + side_expt])
# plt.plot(data[data_center - side_expt : data_center + side_expt])
plt.title(f"{strain_label} in dynamic test")
set_labels("Microstrain", "Time")
plt.tight_layout()
plt.savefig(
c.get_image_path("validation/truck-1", "time-series-strain.pdf"))
plt.close()