NeuralBlitz Civil Engineering
What I Do
I provide comprehensive civil engineering tools including structural analysis, geotechnical calculations, hydraulics, transportation engineering, and construction management for infrastructure applications.
When to Use Me
- Structural analysis and design
- Foundation design
- Hydrological calculations
- Transportation planning
- Construction scheduling
- Building codes compliance
Core Concepts
- Structural Analysis: Trusses, beams, frames
- Geotechnics: Soil mechanics, foundations
- Hydraulics: Open channel, pipe flow
- Transportation: Traffic flow, pavement design
- Construction: Critical path, cost estimation
- Materials: Concrete, steel, composites
- Wind Engineering: Loads, aerodynamics
- Seismic Design: Response spectra, base isolation
Code Examples
Structural Analysis
import numpy as np
def shear_force(V, a, L, w):
return V - w * a
def bending_moment(M, V, a, L, w):
return M + V * a - w * a**2 / 2
def deflection_beam(w, L, E, I):
x = np.linspace(0, L, 100)
y = (w * x**2 / (24 * E * I)) * (L**2 - 2 * L * x + x**2)
return x, y
def influence_line_moment(span_length, position):
a = position
b = span_length - position
return a * b / span_length
def moment_distribution(method, stiffness, distribution_factors):
pass
E_steel = 200e9 # Pa
I_beam = 1e-4 # m^4
L = 5 # m
w = 10e3 # N/m
x, y = deflection_beam(w, L, E_steel, I_beam)
print(f"Max deflection: {max(y):.6f} m")
Geotechnical Engineering
def effective_stress(total_stress, pore_pressure):
return total_stress - pore_pressure
def bearing_capacity_qu(c, gamma, Df, B, phi):
Nc = (np.exp(np.pi * np.tan(np.radians(phi))) * np.tan(np.radians(45 + phi/2))**2 - 1) / (np.tan(np.radians(phi)))
Nq = np.exp(np.pi * np.tan(np.radians(phi))) * np.tan(np.radians(45 + phi/2))**2
Ngamma = 2 * (Nc + 1) * np.tan(np.radians(phi))
qu = c * Nc + gamma * Df * Nq + 0.5 * gamma * B * Ngamma
return qu
def settlement_compressible(S, mv, delta_p, B):
return S = mv * delta_p * B
def consolidation_time_factor(Tv, H_drain):
return Tv * H_drain**2 / cv
def slope_stability_fs(c, phi, gamma, H, beta):
Ns = (2 * c) / (gamma * H * np.sin(beta) * np.cos(np.radians(phi)))
return Ns / np.tan(np.radians(45 + phi/2))
c = 20e3 # Pa
phi = 30 # degrees
gamma = 18e3 # N/m³
B = 2 # m
qu = bearing_capacity_qu(c, gamma, 0, B, phi)
print(f"Bearing capacity: {qu/1000:.2f} kPa")
Hydraulics
def mannings_velocity(n, R, S):
return (1/n) * R**(2/3) * S**0.5
def hydraulic_radius(A, P):
return A / P
def critical_depth(q, g=9.81):
return (q**2 / g)**(1/3)
def specific_energy(y, alpha, q, g=9.81):
return y + alpha * q**2 / (2 * g * y**2)
def darcy_weisbach(f, L, D, V, g=9.81):
return f * (L/D) * (V**2 / (2*g))
n = 0.013 # Manning's n for concrete
R = 0.5 # m
S = 0.001 # slope
V = mannings_velocity(n, R, S)
print(f"Flow velocity: {V:.2f} m/s")
Q = 5 # m³/s per m width
yc = critical_depth(Q)
print(f"Critical depth: {yc:.3f} m")
Transportation Engineering
def stopping_sight_distance(V, f, G, t_reaction=2.5, g=9.81):
d_perception = V * t_reaction
d_braking = V**2 / (2 * g * (f + G/100))
return d_perception + d_braking
def level_of_service(v_c, v_free, k, c):
return v_c / v_free
def pavement_layer_thickness(ESAL, SN, layer_coefficients):
D1 = ESAL * layer_coefficients['a1'] / SN
D2 = ESAL * layer_coefficients['a2'] / SN
return D1, D2
def traffic_signal_timing(cycle_length, green_ratio):
green_time = cycle_length * green_ratio
yellow_time = 3
all_red = 2
return {'green': green_time, 'yellow': yellow_time, 'all_red': all_red}
V = 100 # km/h
f = 0.35 # friction factor
G = 2 # percent grade
SSD = stopping_sight_distance(V/3.6, f, G)
print(f"Stopping sight distance: {SSD:.2f} m")
Construction Management
def critical_path_method(activities, precedence):
pass
def cost_time_tradeoff(normal_cost, crash_cost, normal_time, crash_time):
slope = (crash_cost - normal_cost) / (normal_time - crash_time)
return slope
def earned_value_management(BAC, PV, EV, AC):
CV = EV - AC
SV = EV - PV
CPI = EV / AC
SPI = EV / PV
EAC = BAC / CPI
ETC = EAC - AC
return {
'CV': CV, 'SV': SV,
'CPI': CPI, 'SPI': SPI,
'EAC': EAC, 'ETC': ETC
}
def concrete_cylinder_strength(fc_prime, age, k1=0.79, age_ref=28):
return fc_prime * (age / (k1 + (1-k1) * age/age_ref))**0.5
BAC, PV, EV, AC = 1000000, 250000, 200000, 275000
evm = earned_value_management(BAC, PV, EV, AC)
print(f"CPI: {evm['CPI']:.2f}")
print(f"SPI: {evm['SPI']:.2f}")
Best Practices
- Safety Factors: Apply appropriate factors of safety
- Building Codes: Follow local codes and standards
- Material Properties: Use appropriate material properties
- Load Combinations: Consider all load cases
- Site Conditions: Account for actual site conditions
Common Patterns
# Concrete mix design
def concrete_mix_design(fc, max_aggregate, slump):
pass
Core Competencies
- Structural analysis and design
- Geotechnical engineering
- Hydraulics and hydrology
- Transportation engineering
- Construction management