Architecture Design Guide

Design Thinking Process

Structural Systems

Structural Logic for Researchers

Primary structural systems and their research applications:

FRAME STRUCTURES
- Material: Steel, reinforced concrete, timber
- Behavior: Moment-resisting frames, braced frames
- Research topics: Connection design, progressive collapse,
  fire resistance, seismic performance

SHELL STRUCTURES
- Types: Vaults, domes, hyperbolic paraboloids
- Behavior: Membrane forces (compression/tension), minimal bending
- Research topics: Form-finding, buckling, computational geometry
- Key method: Thrust network analysis (Block, 2009)

TENSILE STRUCTURES
- Types: Cable-stayed, membrane, tensegrity
- Behavior: Pure tension, anticlastic curvature
- Research topics: Form-finding, wind loading, material durability
- Key method: Dynamic relaxation, force density method

MASS TIMBER
- Types: CLT (cross-laminated timber), glulam, mass plywood
- Behavior: Orthotropic, fire charring provides structural reserve
- Research topics: Tall timber buildings, connections, moisture
- Growing field: embodied carbon advantages over concrete/steel

Structural Analysis Methods

# Simple structural analysis for research
# Finite element analysis using Python (for educational purposes)

import numpy as np

def analyze_simply_supported_beam(
    length: float,       # meters
    load: float,         # kN/m (uniform distributed load)
    E: float,            # Young's modulus (GPa)
    I: float,            # Moment of inertia (mm^4)
    n_elements: int = 20,
) -> dict:
    """
    Analyze a simply supported beam under uniform load.
    Returns deflection, moment, and shear diagrams.
    """
    x = np.linspace(0, length, n_elements + 1)
    dx = length / n_elements

    # Analytical solutions
    # Maximum moment: wL^2/8
    max_moment = load * length**2 / 8  # kN-m

    # Moment diagram: M(x) = (w*x/2)*(L-x)
    moment = (load * x / 2) * (length - x)

    # Shear diagram: V(x) = w*(L/2 - x)
    shear = load * (length / 2 - x)

    # Deflection: delta(x) = (w*x)/(24*E*I) * (L^3 - 2*L*x^2 + x^3)
    E_pa = E * 1e9  # Convert GPa to Pa
    I_m4 = I * 1e-12  # Convert mm^4 to m^4
    w_nm = load * 1e3  # Convert kN/m to N/m
    deflection = (w_nm * x) / (24 * E_pa * I_m4) * (
        length**3 - 2 * length * x**2 + x**3
    )
    max_deflection = 5 * w_nm * length**4 / (384 * E_pa * I_m4)

    return {
        "x": x,
        "moment_kNm": moment,
        "shear_kN": shear,
        "deflection_mm": deflection * 1000,
        "max_moment_kNm": max_moment,
        "max_deflection_mm": max_deflection * 1000,
        "span_to_deflection_ratio": length / max_deflection if max_deflection > 0 else float("inf"),
    }

Sustainability and Performance

Building Performance Metrics

Passive Design Strategies

Climate-responsive design strategies:

HOT-ARID CLIMATE
- Thermal mass: Thick walls absorb daytime heat, release at night
- Courtyard typology: Microclimate creation, stack ventilation
- Shading: Deep overhangs, mashrabiya screens
- Evaporative cooling: Water features, vegetation

HOT-HUMID CLIMATE
- Cross ventilation: Openings on opposite facades
- Elevated structures: Raise above ground for air circulation
- Lightweight construction: Low thermal mass (rapid cooling)
- Solar shading: Extended roofs, louvers

COLD CLIMATE
- Compact form: Minimize surface-to-volume ratio
- Superinsulation: R-40+ walls, R-60+ roof, triple glazing
- Solar gain: South-facing glazing (N hemisphere), thermal storage
- Air tightness: < 0.6 ACH50 (Passive House standard)

TEMPERATE CLIMATE
- Mixed-mode: Natural ventilation + mechanical when needed
- Balanced glazing: Optimize daylight vs. heat gain/loss
- Thermal mass: Moderate mass with night purge ventilation
- Responsive facades: Operable shading, automated louvers

Computational Design Methods

Parametric Design with Grasshopper/Python

# Example: Parametric facade panel generation
# (Illustrative -- actual implementation uses Grasshopper + RhinoCommon)

import numpy as np

def generate_parametric_facade(
    width: float,
    height: float,
    panel_count_x: int,
    panel_count_y: int,
    solar_data: np.ndarray,  # Solar radiation per panel position
    min_opening: float = 0.1,
    max_opening: float = 0.8,
) -> list:
    """
    Generate facade panel openings responsive to solar radiation.
    Higher radiation = smaller opening (reduce heat gain).
    """
    panels = []
    for i in range(panel_count_x):
        for j in range(panel_count_y):
            radiation = solar_data[i, j]
            normalized = (radiation - solar_data.min()) / (solar_data.max() - solar_data.min())
            # Inverse: more sun = smaller opening
            opening_ratio = max_opening - normalized * (max_opening - min_opening)

            panels.append({
                "position": (i * width / panel_count_x, j * height / panel_count_y),
                "size": (width / panel_count_x, height / panel_count_y),
                "opening_ratio": round(opening_ratio, 3),
                "solar_radiation": round(radiation, 1),
            })
    return panels

BIM for Research

Building Information Modeling (BIM) in academic research:

APPLICATIONS:
1. Performance simulation integration (energy, daylight, acoustics)
2. Construction logistics and scheduling research
3. Facility management and digital twin studies
4. Heritage documentation and conservation
5. Parametric and generative design exploration

INTEROPERABILITY STANDARDS:
- IFC (Industry Foundation Classes): Open BIM exchange format
- gbXML: Energy simulation data exchange
- COBie: Facility management data
- CityGML: Urban-scale modeling

RESEARCH TOOLS:
- Revit + Dynamo: Parametric BIM authoring
- ArchiCAD + Grasshopper Live Connection
- BlenderBIM: Open-source BIM (IFC native)
- OpenStudio + EnergyPlus: Energy simulation
- Ladybug/Honeybee: Environmental analysis (Grasshopper)

Best Practices

• Ground design claims in evidence. Post-occupancy evaluation and simulation results strengthen design arguments.
• Use multiple performance metrics. Energy, daylight, thermal comfort, and embodied carbon often trade off against each other.
• Document design rationale. The reasoning behind decisions is as important as the decisions themselves.
• Engage with architectural theory. Computational methods are tools, not substitutes for critical thinking about space, meaning, and experience.
• Validate simulations against measured data. Calibrated models are far more credible than uncalibrated predictions.
• Consider the full lifecycle. Embodied carbon in materials can exceed operational carbon over 60 years.

References

• Ching, F. D. K. (2014). Architecture: Form, Space, and Order, 4th ed. Wiley.
• ArchDaily -- Architectural project database and analysis
• Ladybug Tools -- Environmental analysis for Grasshopper
• Block, P. et al. (2017). Beyond Bending: Reimagining Compression Shells. DETAIL.
• RIBA 2030 Climate Challenge -- Carbon benchmarks