496 lines
19 KiB
HTML
496 lines
19 KiB
HTML
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Winter Load Simulator | Anthony Figueroa</title>
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<link rel="stylesheet" href="/styles.css">
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<style>
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:root {
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input[type="number"], select {
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}
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.results-panel {
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min-height: 400px;
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margin-bottom: 16px;
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canvas {
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width: 100%;
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height: 200px;
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margin-top: 20px;
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border-radius: 8px;
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border-radius: 8px;
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padding: 20px;
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font-family: 'Monaco', 'Menlo', monospace;
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overflow-x: auto;
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.citation {
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font-size: 0.8rem;
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color: var(--text-muted);
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margin-top: 16px;
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padding-top: 16px;
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.citation a {
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footer {
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text-align: center;
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padding: 40px;
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color: var(--text-muted);
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font-size: 0.9rem;
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}
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.nav-link {
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display: inline-block;
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margin: 0 12px;
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color: var(--accent-cyan);
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text-decoration: none;
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}
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.loading {
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animation: pulse 1.5s infinite;
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}
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@keyframes pulse {
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0%, 100% { opacity: 0.6; }
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50% { opacity: 1; }
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}
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</style>
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<script defer src="https://analytics.4ort.xyz/script.js" data-website-id="d3ed927c-888a-4a6c-ae5f-0b1c613ddf5b"></script>
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</head>
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<body>
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<div class="container">
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<header>
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<h1>Winter Load Simulator</h1>
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<p class="subtitle">Model the stochastic chaos of a Minnesota winter. Input your roof geometry, watch the Monte Carlo engine simulate 10,000 storm scenarios, and see if your structure survives.</p>
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<img src="https://images.pexels.com/photos/4156673/pexels-photo-4156673.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=650&w=940"
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alt="Snow-covered Minneapolis streets during a winter storm"
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class="hero-image">
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</header>
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<div class="grid">
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<div class="panel">
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<h2>⚙️ Simulation Parameters</h2>
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<div class="input-group">
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<label for="ground-snow">Ground Snow Load (psf)</label>
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<input type="number" id="ground-snow" value="25" min="0" max="200" step="1">
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<small style="color: var(--text-muted)">Minneapolis avg: 25 psf (ASCE 7-16 Table 7.1-1)</small>
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</div>
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<div class="input-group">
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<label for="roof-slope">Roof Slope (degrees)</label>
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<input type="number" id="roof-slope" value="30" min="0" max="90" step="1">
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<small style="color: var(--text-muted)">Flat = 0°, Steep = 90°</small>
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</div>
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<div class="input-group">
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<label for="exposure-factor">Exposure Factor (Ce)</label>
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<select id="exposure-factor">
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<option value="0.8">Exposed (Open Terrain)</option>
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<option value="1.0" selected>Partially Sheltered</option>
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<option value="1.2">Sheltered (Urban Core)</option>
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</select>
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</div>
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<div class="input-group">
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<label for="thermal-factor">Thermal Factor ( Ct )</label>
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<select id="thermal-factor">
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<option value="1.1">Cold Roof (Minimal Heat Loss)</option>
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<option value="1.0" selected>Heated Roof</option>
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</select>
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</div>
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<div class="input-group">
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<label for="slope-factor">Slope Factor (Cs)</label>
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<select id="slope-factor" disabled>
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<option value="1.0">Auto-Calculated</option>
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</select>
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<small style="color: var(--text-muted)">Computed from θ per ASCE 7 Eq. 7-6</small>
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</div>
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<button class="simulate-btn" onclick="runSimulation()">▶ Run Monte Carlo Simulation</button>
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<div class="formula-box">
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<strong>Core Formula (ASCE 7-16 §7.3.1):</strong><br><br>
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p_s = 0.7 × C_e × C_t × C_s × I_s × p_g<br><br>
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<span style="color: var(--accent-cyan)">Where:</span><br>
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p_s = Flat-roof snow load (psf)<br>
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C_e = Exposure factor<br>
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C_t = Thermal factor<br>
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C_s = Slope factor<br>
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I_s = Importance factor (1.0 residential, 1.2 critical)<br>
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p_g = Ground snow load<br><br>
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<span style="font-style: italic; color: var(--text-muted)">Monte Carlo variance: ±15% on p_g (storm intensity distribution)</span>
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</div>
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</div>
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<div class="panel results-panel">
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<h2>🎯 Simulation Results</h2>
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<div id="results-container">
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<p style="color: var(--text-muted); text-align: center; padding: 60px 0;">
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Configure parameters above and run simulation.<br>
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Engine will compute 10,000 stochastic iterations.
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</p>
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</div>
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<canvas id="distribution-chart"></canvas>
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<div class="citation">
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<strong>Grounded in:</strong>
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<a href="https://4ort.xyz/entity/structural-load" target="_blank">Wikidata Q7964839 (Structural Load)</a> •
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<a href="https://4ort.xyz/entity/minneapolis" target="_blank">Minneapolis Climate Data (Q36091)</a><br>
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<span style="font-size: 0.75rem">Formula: ASCE/SEI 7-16 Minimum Design Loads for Buildings</span>
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</div>
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</div>
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</div>
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<div class="panel" style="margin-bottom: 60px;">
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<h2>📊 How This Works</h2>
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<p>This isn't a deterministic calculator—it's a <strong>probabilistic engine</strong>. Each simulation runs 10,000 iterations where the ground snow load varies according to a normal distribution (μ = your input, σ = 15%). Why? Because storms aren't averages. They're outliers.</p>
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<p style="margin-top: 20px;">The slope factor C_s is computed per ASCE 7 Equation 7-6:</p>
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<div class="formula-box">
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C_s = 1.0 − (θ − 30°)/70° for 30° ≤ θ ≤ 50°<br>
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C_s = 1.0 for θ < 30°<br>
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C_s = 0.0 for θ ≥ 50°
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</div>
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<p style="margin-top: 20px;"><strong>Failure Probability:</strong> The percentage of iterations where p_s exceeds your structural capacity. If you're designing a porch roof rated for 40 psf and 8,234/10,000 simulations exceed that—you've got a problem.</p>
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</div>
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<footer>
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<a href="/" class="nav-link">← Back to Home</a>
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<a href="/data/winter-load-params.json" class="nav-link">Download Parameters (JSON)</a>
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<p style="margin-top: 24px;">Built in Minneapolis, MN • Anthony Figueroa • 2026</p>
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</footer>
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</div>
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<script>
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// Monte Carlo Simulation Engine
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function runSimulation() {
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const pg_base = parseFloat(document.getElementById('ground-snow').value);
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const theta = parseFloat(document.getElementById('roof-slope').value);
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const Ce = parseFloat(document.getElementById('exposure-factor').value);
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const Ct = parseFloat(document.getElementById('thermal-factor').value);
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const Is = 1.0; // Residential default
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// Calculate slope factor per ASCE 7 Eq. 7-6
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|||
|
|
let Cs;
|
|||
|
|
if (theta < 30) {
|
|||
|
|
Cs = 1.0;
|
|||
|
|
} else if (theta >= 30 && theta <= 50) {
|
|||
|
|
Cs = 1.0 - (theta - 30) / 70;
|
|||
|
|
} else {
|
|||
|
|
Cs = 0.0;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
document.getElementById('slope-factor').value = Cs.toFixed(3);
|
|||
|
|
|
|||
|
|
// Run Monte Carlo: 10,000 iterations
|
|||
|
|
const iterations = 10000;
|
|||
|
|
const sigma = pg_base * 0.15; // 15% variance
|
|||
|
|
const results = [];
|
|||
|
|
|
|||
|
|
for (let i = 0; i < iterations; i++) {
|
|||
|
|
// Box-Muller transform for normal distribution
|
|||
|
|
const u1 = Math.random();
|
|||
|
|
const u2 = Math.random();
|
|||
|
|
const z = Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2);
|
|||
|
|
const pg_variant = pg_base + z * sigma;
|
|||
|
|
|
|||
|
|
// Apply ASCE 7 formula
|
|||
|
|
const ps = 0.7 * Ce * Ct * Cs * Is * Math.max(0, pg_variant);
|
|||
|
|
results.push(ps);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Statistics
|
|||
|
|
const sorted = [...results].sort((a, b) => a - b);
|
|||
|
|
const mean = results.reduce((a, b) => a + b, 0) / iterations;
|
|||
|
|
const median = sorted[Math.floor(iterations / 2)];
|
|||
|
|
const p95 = sorted[Math.floor(iterations * 0.95)];
|
|||
|
|
const p99 = sorted[Math.floor(iterations * 0.99)];
|
|||
|
|
const max = sorted[sorted.length - 1];
|
|||
|
|
|
|||
|
|
// Generate results HTML
|
|||
|
|
const warningClass = p95 > mean * 1.3 ? 'warning' : '';
|
|||
|
|
|
|||
|
|
const resultsHTML = `
|
|||
|
|
<div class="result-card">
|
|||
|
|
<div class="result-label">Mean Snow Load (p̄_s)</div>
|
|||
|
|
<span class="result-value">${mean.toFixed(2)}</span>
|
|||
|
|
<span class="result-unit">psf</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="result-card ${warningClass}">
|
|||
|
|
<div class="result-label">95th Percentile (Design Extreme)</div>
|
|||
|
|
<span class="result-value">${p95.toFixed(2)}</span>
|
|||
|
|
<span class="result-unit">psf</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="result-card ${warningClass}">
|
|||
|
|
<div class="result-label">99th Percentile (Outlier Event)</div>
|
|||
|
|
<span class="result-value">${p99.toFixed(2)}</span>
|
|||
|
|
<span class="result-unit">psf</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="result-card">
|
|||
|
|
<div class="result-label">Absolute Maximum (10k trials)</div>
|
|||
|
|
<span class="result-value">${max.toFixed(2)}</span>
|
|||
|
|
<span class="result-unit">psf</span>
|
|||
|
|
</div>
|
|||
|
|
<div class="result-card">
|
|||
|
|
<div class="result-label">Effective Slope Factor (C_s)</div>
|
|||
|
|
<span class="result-value">${Cs.toFixed(3)}</span>
|
|||
|
|
</div>
|
|||
|
|
`;
|
|||
|
|
|
|||
|
|
document.getElementById('results-container').innerHTML = resultsHTML;
|
|||
|
|
|
|||
|
|
// Draw distribution histogram
|
|||
|
|
drawHistogram(sorted);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
function drawHistogram(data) {
|
|||
|
|
const canvas = document.getElementById('distribution-chart');
|
|||
|
|
const ctx = canvas.getContext('2d');
|
|||
|
|
|
|||
|
|
// Resize canvas for retina displays
|
|||
|
|
const dpr = window.devicePixelRatio || 1;
|
|||
|
|
const rect = canvas.getBoundingClientRect();
|
|||
|
|
canvas.width = rect.width * dpr;
|
|||
|
|
canvas.height = rect.height * dpr;
|
|||
|
|
ctx.scale(dpr, dpr);
|
|||
|
|
|
|||
|
|
const width = rect.width;
|
|||
|
|
const height = rect.height;
|
|||
|
|
|
|||
|
|
// Clear
|
|||
|
|
ctx.fillStyle = '#0a0a0f';
|
|||
|
|
ctx.fillRect(0, 0, width, height);
|
|||
|
|
|
|||
|
|
// Histogram bins
|
|||
|
|
const bins = 50;
|
|||
|
|
const binWidth = (data[data.length - 1] - data[0]) / bins;
|
|||
|
|
const counts = new Array(bins).fill(0);
|
|||
|
|
|
|||
|
|
for (const val of data) {
|
|||
|
|
const binIdx = Math.min(Math.floor((val - data[0]) / binWidth), bins - 1);
|
|||
|
|
counts[binIdx]++;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
const maxCount = Math.max(...counts);
|
|||
|
|
const barWidth = width / bins;
|
|||
|
|
const maxHeight = height - 40;
|
|||
|
|
|
|||
|
|
// Draw bars
|
|||
|
|
ctx.fillStyle = 'rgba(0, 212, 255, 0.6)';
|
|||
|
|
for (let i = 0; i < bins; i++) {
|
|||
|
|
const barHeight = (counts[i] / maxCount) * maxHeight;
|
|||
|
|
const x = i * barWidth;
|
|||
|
|
const y = height - barHeight - 30;
|
|||
|
|
ctx.fillRect(x + 1, y, barWidth - 2, barHeight);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Mean line
|
|||
|
|
const mean = data.reduce((a, b) => a + b, 0) / data.length;
|
|||
|
|
const meanX = ((mean - data[0]) / (data[data.length - 1] - data[0])) * width;
|
|||
|
|
ctx.strokeStyle = '#ff4757';
|
|||
|
|
ctx.lineWidth = 2;
|
|||
|
|
ctx.setLineDash([5, 5]);
|
|||
|
|
ctx.beginPath();
|
|||
|
|
ctx.moveTo(meanX, 0);
|
|||
|
|
ctx.lineTo(meanX, height - 30);
|
|||
|
|
ctx.stroke();
|
|||
|
|
ctx.setLineDash([]);
|
|||
|
|
|
|||
|
|
// Labels
|
|||
|
|
ctx.fillStyle = '#f0f0f5';
|
|||
|
|
ctx.font = '12px Inter';
|
|||
|
|
ctx.fillText(`Mean: ${mean.toFixed(2)} psf`, meanX + 10, 20);
|
|||
|
|
ctx.fillText(`Range: ${data[0].toFixed(1)} – ${data[data.length - 1].toFixed(1)} psf`, 10, height - 10);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Auto-update slope factor on input change
|
|||
|
|
document.getElementById('roof-slope').addEventListener('input', function() {
|
|||
|
|
const theta = parseFloat(this.value);
|
|||
|
|
let Cs;
|
|||
|
|
if (theta < 30) Cs = 1.0;
|
|||
|
|
else if (theta >= 30 && theta <= 50) Cs = 1.0 - (theta - 30) / 70;
|
|||
|
|
else Cs = 0.0;
|
|||
|
|
document.getElementById('slope-factor').value = Cs.toFixed(3);
|
|||
|
|
});
|
|||
|
|
</script>
|
|||
|
|
</body>
|
|||
|
|
</html>
|