{"id":3478,"date":"2023-08-31T11:12:59","date_gmt":"2023-08-31T11:12:59","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/concrete-properties-assesment\/"},"modified":"2026-08-25T14:03:25","modified_gmt":"2026-08-25T12:03:25","slug":"concrete-properties-assesment","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/concrete-properties-assesment\/","title":{"rendered":"Model concrete properties using machine learning"},"content":{"rendered":"\n<style>\n.ndb{width:100vw;margin-left:calc(50% - 50vw);padding:22px 24px 14px;background:#eee;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}.ndb *{box-sizing:border-box}.ndb-wrap{width:min(100%,1200px);margin:0 auto}.ndb a{text-decoration:none;color:#2d799f;font-weight:600}\n.ndb-executive{margin:0 0 34px;padding:32px;border-radius:20px;background:linear-gradient(135deg,#12354b,#245e80);color:#fff;box-shadow:0 16px 36px rgba(0,18,51,.18)}.ndb-executive h2{margin:0 0 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iframe{position:absolute;inset:0;width:100%;height:100%;border:0;border-radius:12px;box-shadow:0 12px 28px rgba(0,18,51,.14)}\n@media(max-width:560px){.ndb-output-grid{grid-template-columns:1fr}}\n<\/style>\n\n<div class=\"ndb\"><div class=\"ndb-wrap\">\n<section class=\"ndb-executive\"><h2>Screen 28-day concrete strength before committing to laboratory trials<\/h2>\n<p>This surrogate model estimates 28-day compressive strength from seven constituent quantities. It helps concrete technologists compare candidate mixtures and focus physical testing on the most promising, feasible formulations.<\/p>\n<div class=\"ndb-kpis\"><div class=\"ndb-kpi\"><strong>425<\/strong><span>28-day laboratory specimens<\/span><\/div><div class=\"ndb-kpi\"><strong>7<\/strong><span>mixture-component inputs<\/span><\/div><div class=\"ndb-kpi\"><strong>85<\/strong><span>independent testing rows<\/span><\/div><div class=\"ndb-kpi\"><strong>4.88 MPa<\/strong><span>testing MAE<\/span><\/div><\/div>\n<div class=\"ndb-actions\"><a href=\"#7-model-deployment\">Try the model<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/10\/concrete_properties.csv\">Download concrete_properties.csv<\/a><\/div><\/section>\n<div class=\"ndb-lead\"><p>Compressive-strength testing is essential, but each result arrives only after batching, curing and destructive laboratory testing. A data-driven surrogate can provide rapid estimates for screening and scenario analysis, while qualified mix design, trial batches and standards-based testing remain the basis for production and structural decisions.<\/p><\/div>\n<ul class=\"ndb-toc\"><li><a href=\"#1-industrial-challenge\">Industrial challenge<\/a><\/li><li><a href=\"#2-data-set\">Data set<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Model selection<\/a><\/li><li><a href=\"#6-testing\">Testing<\/a><\/li><li><a href=\"#7-model-deployment\">Deployment<\/a><\/li><li><a href=\"#8-limitations\">Limitations<\/a><\/li><li><a href=\"#references\">References<\/a><\/li><\/ul>\n\n<section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Industrial challenge<\/h2>\n<p>This is an <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Approximation\">approximation<\/a> problem: the model maps seven constituent quantities to the continuous target <code>compressive_strength<\/code>, measured in MPa after 28 days.<\/p>\n<div class=\"ndb-value-grid\"><div class=\"ndb-value\"><strong>Screen candidate mixtures<\/strong><span>Estimate whether a formulation is likely to approach the required strength before scheduling a complete trial.<\/span><\/div><div class=\"ndb-value\"><strong>Study material trade-offs<\/strong><span>Explore how cement, supplementary cementitious materials, water, admixture and aggregates jointly affect the response.<\/span><\/div><div class=\"ndb-value\"><strong>Prioritize laboratory work<\/strong><span>Use predictions to rank feasible candidates while retaining physical testing for verification and acceptance.<\/span><\/div><\/div>\n<p>Potential users include ready-mix and precast producers, concrete technologists, materials laboratories, civil and structural engineers, quality teams and low-carbon construction specialists.<\/p>\n<div class=\"ndb-audience\"><span>Concrete technology<\/span><span>Ready-mix production<\/span><span>Precast manufacturing<\/span><span>Materials laboratories<\/span><span>Quality assurance<\/span><span>Sustainable construction<\/span><\/div>\n<div class=\"ndb-note\"><strong>Model role.<\/strong> This is a 28-day strength-screening surrogate. It supports engineering exploration but does not issue an approved mix design, verify code compliance or replace batch trials and destructive tests.<\/div><\/section>\n\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2>\n<p>The source <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/165\/concrete%2B\">UCI Concrete Compressive Strength dataset<\/a> contains 1,030 laboratory results with age as an input. This example uses all <strong>425 records tested at 28 days<\/strong>, so age is fixed and omitted from the local file. The semicolon-delimited CSV contains seven numerical inputs, one numerical target and no missing values.<\/p>\n<div class=\"ndb-downloads\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/10\/concrete_properties.csv\">Download dataset: concrete_properties.csv<\/a><\/div>\n<table><thead><tr><th>CSV variable<\/th><th>Engineering meaning<\/th><th>Role<\/th><th>Unit<\/th><th>Observed range<\/th><\/tr><\/thead><tbody><tr><td><code>cement<\/code><\/td><td>Cement<\/td><td>Input<\/td><td>kg\/m\u00b3<\/td><td>102 to 540<\/td><\/tr><tr><td><code>blast_furnace_slag<\/code><\/td><td>Blast-furnace slag<\/td><td>Input<\/td><td>kg\/m\u00b3<\/td><td>0 to 359.4<\/td><\/tr><tr><td><code>fly_ash<\/code><\/td><td>Fly ash<\/td><td>Input<\/td><td>kg\/m\u00b3<\/td><td>0 to 200.1<\/td><\/tr><tr><td><code>water<\/code><\/td><td>Water<\/td><td>Input<\/td><td>kg\/m\u00b3<\/td><td>121.8 to 247<\/td><\/tr><tr><td><code>superplasticizer<\/code><\/td><td>Superplasticizer<\/td><td>Input<\/td><td>kg\/m\u00b3<\/td><td>0 to 32.2<\/td><\/tr><tr><td><code>coarse_aggregate<\/code><\/td><td>Coarse aggregate<\/td><td>Input<\/td><td>kg\/m\u00b3<\/td><td>801 to 1145<\/td><\/tr><tr><td><code>fine_aggregate<\/code><\/td><td>Fine aggregate<\/td><td>Input<\/td><td>kg\/m\u00b3<\/td><td>594 to 992.6<\/td><\/tr><tr><td><code>compressive_strength<\/code><\/td><td>28-day compressive strength<\/td><td>Target<\/td><td>MPa<\/td><td>8.54 to 81.75<\/td><\/tr><\/tbody><\/table>\n<p>The configured random split assigns 255 rows to training, 85 to selection and 85 to testing.<\/p>\n<img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-strength-distribution-2026.png\" alt=\"Distribution of 28-day concrete compressive strength across 425 specimens\">\n<div class=\"ndb-figure-grid\"><figure><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-strength-correlations-2026.png\" alt=\"Pearson correlations between concrete constituents and 28-day compressive strength\"><figcaption><strong>Marginal relationships.<\/strong> Cement has the strongest positive linear correlation, while water has the strongest negative one. These one-variable relationships do not describe mixture interactions.<\/figcaption><\/figure><figure><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-strength-cement-scatter-2026.png\" alt=\"Scatter chart of cement content and 28-day concrete compressive strength\"><figcaption><strong>Cement alone is insufficient.<\/strong> Strength varies substantially at similar cement contents because the complete formulation matters.<\/figcaption><\/figure><\/div>\n<div class=\"ndb-note ndb-note--warning\"><strong>Validation note.<\/strong> The split is random. The file contains 416 unique input formulations and five duplicated formulation groups; two duplicate groups cross subset boundaries. A stronger evaluation should group identical or related mixtures so that one formulation cannot contribute rows to both model development and testing.<\/div><\/section>\n\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Neural network<\/h2>\n<p>The initial model standardizes seven inputs using mean and standard deviation, evaluates three tanh hidden neurons and returns one linear strength estimate before unscaling to MPa. The initial 7\u20133\u20131 architecture contains 28 trainable parameters.<\/p>\n<img decoding=\"async\" class=\"ndb-architecture ndb-architecture--wide\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-strength-initial-network-2026.png\" alt=\"Initial concrete strength neural network with seven inputs, three tanh neurons and one output\">\n<p>The initial network provides a compact starting point. Model selection subsequently changes the hidden-layer size; the final testing and deployment model is shown in section 5.<\/p><\/section>\n\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2>\n<p>The initial network minimizes normalized squared error with L2 regularization weight 0.01 using the quasi-Newton method. Optimization stops after 78 epochs because the minimum loss decrease criterion is reached. The recorded training and selection errors are <strong>0.098 NSE<\/strong> and <strong>0.049 NSE<\/strong>.<\/p>\n<img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-strength-training-history-2026.png\" alt=\"Quasi-Newton training and selection error history for the initial concrete strength model\">\n<p>Normalized squared error is useful during optimization, but MPa-based metrics are used later so engineering users can interpret the practical size of prediction errors.<\/p><\/section>\n\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2>\n<p>The growing-neurons task compares hidden-layer sizes from 1 to 10, using three trials per size. The smallest recorded selection error, <strong>0.0429 NSE<\/strong>, occurs at 10 neurons; the corresponding training error is 0.083 NSE.<\/p>\n<img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-strength-neuron-selection-2026.png\" alt=\"Training and selection errors for concrete models with one to ten hidden neurons\">\n<p>Because the best result occurs at the configured maximum, 10 neurons is the best tested size rather than proof of a global optimum. The final 7\u201310\u20131 network contains 91 trainable parameters and is the model used for testing, optimization and Python export.<\/p>\n<img decoding=\"async\" class=\"ndb-architecture ndb-architecture--wide\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-strength-final-network-2026.png\" alt=\"Final concrete strength neural network with seven inputs, ten tanh neurons and one output\"><\/section>\n\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2>\n<p>The exported final model was recalculated on the 85 rows marked as testing. R\u00b2 is the squared correlation reported by the Neural Designer goodness-of-fit analysis; MAE and RMSE express errors directly in MPa.<\/p>\n<table><thead><tr><th>Testing rows<\/th><th>R\u00b2<\/th><th>MAE<\/th><th>RMSE<\/th><th>Bias<\/th><th>95th-percentile absolute error<\/th><th>Maximum absolute error<\/th><\/tr><\/thead><tbody><tr><td>85<\/td><td>0.8214<\/td><td>4.88 MPa<\/td><td>6.06 MPa<\/td><td>+0.68 MPa<\/td><td>10.70 MPa<\/td><td>19.34 MPa<\/td><\/tr><\/tbody><\/table>\n<p>A training-mean baseline has an RMSE of 14.22 MPa, so the neural model materially improves on a constant prediction. Nevertheless, only 56.5% of testing rows fall within \u00b15 MPa, and the largest error is operationally significant. This supports screening use, not automatic acceptance.<\/p>\n<img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-strength-goodness-of-fit-2026.png\" alt=\"Predicted versus measured 28-day concrete compressive strength on the testing subset\">\n<div class=\"ndb-note\"><strong>Example testing specimen.<\/strong> Row 421 contains 276.4 kg\/m\u00b3 cement, 116.0 kg\/m\u00b3 slag, 90.3 kg\/m\u00b3 fly ash, 179.6 kg\/m\u00b3 water, 8.9 kg\/m\u00b3 superplasticizer, 870.1 kg\/m\u00b3 coarse aggregate and 768.3 kg\/m\u00b3 fine aggregate. The measured strength is 44.28 MPa and the model predicts 44.74 MPa, an absolute error of 0.46 MPa.<\/div><\/section>\n\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2>\n<p>A practical workflow starts with a physically feasible formulation and ends with laboratory confirmation. The model can screen or rank candidates, but engineering constraints must be applied before inference and predictions must not become production specifications without validation.<\/p>\n<div class=\"ndb-flow\"><div>Candidate formulation and material data<\/div><div>Range, mass-balance and feasibility checks<\/div><div>28-day strength surrogate<\/div><div>Trial batch, testing and engineering approval<\/div><\/div>\n<div class=\"ndb-case\"><h3>Optimization case 1: reduce cement for a 45 MPa requirement<\/h3>\n<p><strong>Operational question:<\/strong> what is the lowest-cement candidate that provides a modelled safety margin above a required 28-day strength of 45 MPa?<\/p>\n<p>The executed task minimizes cement, keeps the other constituents inside defined operating intervals and requires a predicted strength of at least <strong>55 MPa<\/strong>. The additional 10 MPa is an illustrative screening margin informed by the model error; it is not a certified characteristic-strength calculation or prediction interval.<\/p>\n<div class=\"ndb-table-scroll\"><table><thead><tr><th>Variable<\/th><th>Condition<\/th><th>Lower bound<\/th><th>Upper bound<\/th><th>Optimized value<\/th><th>Unit<\/th><\/tr><\/thead><tbody>\n<tr><td>Cement<\/td><td>Minimize<\/td><td>\u2014<\/td><td>\u2014<\/td><td>126.513<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Blast-furnace slag<\/td><td>Between<\/td><td>50<\/td><td>220<\/td><td>219.710<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Fly ash<\/td><td>Between<\/td><td>0<\/td><td>150<\/td><td>139.026<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Water<\/td><td>Between<\/td><td>160<\/td><td>195<\/td><td>172.402<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Superplasticizer<\/td><td>Between<\/td><td>0<\/td><td>15<\/td><td>14.277<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Coarse aggregate<\/td><td>Between<\/td><td>850<\/td><td>1,075<\/td><td>1,060.860<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Fine aggregate<\/td><td>Between<\/td><td>650<\/td><td>875<\/td><td>869.364<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Compressive strength<\/td><td>\u2265<\/td><td>55<\/td><td>\u2014<\/td><td>55.000<\/td><td>MPa<\/td><\/tr>\n<\/tbody><\/table><\/div>\n<h3>Result and engineering interpretation<\/h3>\n<table><thead><tr><th>Check<\/th><th>Candidate<\/th><th>Observed data domain<\/th><th>Assessment<\/th><\/tr><\/thead><tbody>\n<tr><td>Total binder<\/td><td>485.249 kg\/m\u00b3<\/td><td>200.0\u2013640.0 kg\/m\u00b3<\/td><td>Inside observed interval<\/td><\/tr>\n<tr><td>Water-to-binder ratio<\/td><td>0.355<\/td><td>0.235\u20130.900<\/td><td>Inside observed interval<\/td><\/tr>\n<tr><td>Total constituent mass<\/td><td>2,602.152 kg\/m\u00b3<\/td><td>2,194.6\u20132,551.0 kg\/m\u00b3<\/td><td>51.2 kg\/m\u00b3 above observed maximum<\/td><\/tr>\n<\/tbody><\/table>\n<p>The optimizer reduces cement by moving slag close to its upper bound and using substantial fly ash, superplasticizer and aggregate quantities. The model reproduces the requested 55 MPa threshold, but the constituent total is approximately 2.0% above the largest formulation in the dataset. The candidate therefore demonstrates a credible low-cement objective, yet it still requires a mass-balance or yield constraint and laboratory validation before it can be considered a feasible mix.<\/p>\n<\/div>\n\n<div class=\"ndb-case\"><h3>Optimization case 2: maximize strength with cement capped at 300 kg\/m\u00b3<\/h3>\n<p><strong>Operational question:<\/strong> what is the highest 28-day strength predicted inside the declared constituent limits when cement cannot exceed 300 kg\/m\u00b3?<\/p>\n<p>This scenario maximizes the model output while bounding cement and every other constituent. It represents a producer seeking higher strength without moving to a high-cement formulation.<\/p>\n<div class=\"ndb-table-scroll\"><table><thead><tr><th>Variable<\/th><th>Condition<\/th><th>Lower bound<\/th><th>Upper bound<\/th><th>Optimized value<\/th><th>Unit<\/th><\/tr><\/thead><tbody>\n<tr><td>Cement<\/td><td>Between<\/td><td>200<\/td><td>300<\/td><td>299.998<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Blast-furnace slag<\/td><td>Between<\/td><td>0<\/td><td>220<\/td><td>220.000<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Fly ash<\/td><td>Between<\/td><td>0<\/td><td>150<\/td><td>149.499<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Water<\/td><td>Between<\/td><td>155<\/td><td>190<\/td><td>178.930<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Superplasticizer<\/td><td>Between<\/td><td>0<\/td><td>15<\/td><td>13.390<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Coarse aggregate<\/td><td>Between<\/td><td>850<\/td><td>1,075<\/td><td>1,075.000<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Fine aggregate<\/td><td>Between<\/td><td>650<\/td><td>875<\/td><td>874.735<\/td><td>kg\/m\u00b3<\/td><\/tr>\n<tr><td>Compressive strength<\/td><td>Maximize<\/td><td>\u2014<\/td><td>\u2014<\/td><td>80.156<\/td><td>MPa<\/td><\/tr>\n<\/tbody><\/table><\/div>\n<h3>Result and engineering interpretation<\/h3>\n<table><thead><tr><th>Check<\/th><th>Candidate<\/th><th>Observed data domain<\/th><th>Assessment<\/th><\/tr><\/thead><tbody>\n<tr><td>Total binder<\/td><td>669.497 kg\/m\u00b3<\/td><td>200.0\u2013640.0 kg\/m\u00b3<\/td><td>29.5 kg\/m\u00b3 above observed maximum<\/td><\/tr>\n<tr><td>Water-to-binder ratio<\/td><td>0.267<\/td><td>0.235\u20130.900<\/td><td>Inside observed interval<\/td><\/tr>\n<tr><td>Total constituent mass<\/td><td>2,811.552 kg\/m\u00b3<\/td><td>2,194.6\u20132,551.0 kg\/m\u00b3<\/td><td>260.6 kg\/m\u00b3 above observed maximum<\/td><\/tr>\n<\/tbody><\/table>\n<p>The solution reaches 80.156 MPa by driving cement, slag, fly ash and both aggregates to, or very close to, their upper bounds. This is a typical boundary-seeking optimizer response. Although the individual values satisfy the declared limits, binder content and total mass lie outside every mixture represented in the data. The result is best interpreted as a diagnostic upper-bound scenario: it shows why a real maximum-strength optimization also needs total-binder, yield, workability, cost and durability constraints.<\/p>\n<\/div>\n\n<h3>Comparison of the two executed cases<\/h3>\n<table><thead><tr><th>Scenario<\/th><th>Primary objective<\/th><th>Cement<\/th><th>Predicted strength<\/th><th>Total mass<\/th><th>Current status<\/th><\/tr><\/thead><tbody>\n<tr><td>Low-cement target-strength case<\/td><td>Minimize cement with predicted strength \u226555 MPa<\/td><td>126.513 kg\/m\u00b3<\/td><td>55.000 MPa<\/td><td>2,602.152 kg\/m\u00b3<\/td><td>Promising objective; revise mass balance<\/td><\/tr>\n<tr><td>Cement-capped maximum-strength case<\/td><td>Maximize strength with cement \u2264300 kg\/m\u00b3<\/td><td>299.998 kg\/m\u00b3<\/td><td>80.156 MPa<\/td><td>2,811.552 kg\/m\u00b3<\/td><td>Boundary result; reformulate constraints<\/td><\/tr>\n<\/tbody><\/table>\n<div class=\"ndb-note\"><strong>Decision rule.<\/strong> Response optimization identifies model-supported candidates, not approved concrete recipes. A candidate should advance only after joint-domain checks, mixture-volume and material-compatibility review, trial batching, curing and standards-based strength testing.<\/div>\n<div class=\"ndb-case\"><h3>Cement sensitivity at a reference formulation<\/h3><p>At 265 kg\/m\u00b3 cement, 86 kg\/m\u00b3 slag, 62 kg\/m\u00b3 fly ash, 183 kg\/m\u00b3 water, 7 kg\/m\u00b3 superplasticizer, 956 kg\/m\u00b3 coarse aggregate and 764 kg\/m\u00b3 fine aggregate, the model predicts 37.47 MPa. The directional plot varies cement while holding the other quantities fixed.<\/p>\n<img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-strength-cement-directional-output-2026.png\" alt=\"Predicted 28-day compressive strength as cement content changes at a fixed reference formulation\">\n<p>Interpret the curve only across the observed cement interval of 102\u2013540 kg\/m\u00b3. The portion beyond 540 kg\/m\u00b3 is model extrapolation and should not support a design decision.<\/p><\/div>\n\n<div class=\"ndb-calculator\" id=\"concrete-calculator\">\n<h3>Try the 28-day strength model<\/h3>\n<p>Enter one mixture or load a verified example. The browser evaluates the exact scaling, weights and output transformation from the exported Python model.<\/p>\n<div class=\"ndb-sample-picker\"><label for=\"cp-preset\">Example mixture<\/label><select id=\"cp-preset\"><option value=\"Reference mixture\">Reference mixture<\/option><option value=\"Testing specimen 421\">Testing specimen 421<\/option><option value=\"Low-cement optimization case\">Low-cement optimization case<\/option><option value=\"Maximum-strength optimization case\">Maximum-strength optimization case<\/option><\/select><\/div>\n<form id=\"concrete-calculator-form\"><div class=\"ndb-calculator-grid\"><div class=\"ndb-field\"><label for=\"cp-0\">Cement (kg\/m\u00b3)<\/label><input id=\"cp-0\" name=\"cement\" type=\"number\" min=\"102.0\" max=\"540.0\" step=\"any\" value=\"265.0\"><small>102 to 540<\/small><\/div><div class=\"ndb-field\"><label for=\"cp-1\">Blast-furnace slag (kg\/m\u00b3)<\/label><input id=\"cp-1\" name=\"blast_furnace_slag\" type=\"number\" min=\"0.0\" max=\"359.4\" step=\"any\" value=\"86.0\"><small>0 to 359.4<\/small><\/div><div class=\"ndb-field\"><label for=\"cp-2\">Fly ash (kg\/m\u00b3)<\/label><input id=\"cp-2\" name=\"fly_ash\" type=\"number\" min=\"0.0\" max=\"200.1\" step=\"any\" value=\"62.0\"><small>0 to 200.1<\/small><\/div><div class=\"ndb-field\"><label for=\"cp-3\">Water (kg\/m\u00b3)<\/label><input id=\"cp-3\" name=\"water\" type=\"number\" min=\"121.8\" max=\"247.0\" step=\"any\" value=\"183.0\"><small>121.8 to 247<\/small><\/div><div class=\"ndb-field\"><label for=\"cp-4\">Superplasticizer (kg\/m\u00b3)<\/label><input id=\"cp-4\" name=\"superplasticizer\" type=\"number\" min=\"0.0\" max=\"32.2\" step=\"any\" value=\"7.0\"><small>0 to 32.2<\/small><\/div><div class=\"ndb-field\"><label for=\"cp-5\">Coarse aggregate (kg\/m\u00b3)<\/label><input id=\"cp-5\" name=\"coarse_aggregate\" type=\"number\" min=\"801.0\" max=\"1145.0\" step=\"any\" value=\"956.0\"><small>801 to 1145<\/small><\/div><div class=\"ndb-field\"><label for=\"cp-6\">Fine aggregate (kg\/m\u00b3)<\/label><input id=\"cp-6\" name=\"fine_aggregate\" type=\"number\" min=\"594.0\" max=\"992.6\" step=\"any\" value=\"764.0\"><small>594 to 992.6<\/small><\/div><\/div>\n<div class=\"ndb-calc-actions\"><button type=\"submit\">Predict strength<\/button><button type=\"button\" id=\"cp-load\">Load selected example<\/button><\/div><\/form>\n<div class=\"ndb-output-grid\" aria-live=\"polite\"><div class=\"ndb-output-card\"><span>Predicted 28-day compressive strength<\/span><strong id=\"cp-output\">\u2014<\/strong><em>Model estimate<\/em><\/div><div class=\"ndb-output-card\"><span>Total constituent mass<\/span><strong id=\"cp-total\">\u2014<\/strong><em>Joint-domain check<\/em><\/div><\/div>\n<p class=\"ndb-calc-status\" id=\"cp-status\">Screening model \u2014 not a certified mix design, conformity assessment or structural acceptance method.<\/p>\n<\/div>\n<script>\n(function(){\nconst form=document.getElementById(\"concrete-calculator-form\");if(!form)return;\nconst ids=[\"cp-0\",\"cp-1\",\"cp-2\",\"cp-3\",\"cp-4\",\"cp-5\",\"cp-6\"],presets={\"Reference mixture\": [265.0, 86.0, 62.0, 183.0, 7.0, 956.0, 764.0], \"Testing specimen 421\": [276.4, 116.0, 90.3, 179.6, 8.9, 870.1, 768.3], \"Low-cement optimization case\": [126.513, 219.71, 139.026, 172.402, 14.277, 1060.86, 869.364], \"Maximum-strength optimization case\": [299.998, 220.0, 149.499, 178.93, 13.39, 1075.0, 874.735]};\nfunction calculate(){\nconst fields=ids.map(id=>document.getElementById(id)),x=fields.map(f=>Number(f.value));\nif(x.some(v=>!Number.isFinite(v))){document.getElementById(\"cp-status\").textContent=\"Enter a valid number in every field.\";return}\nlet outside=false;fields.forEach((f,i)=>{const bad=x[i]<Number(f.min)||x[i]>Number(f.max);f.setAttribute(\"aria-invalid\",bad?\"true\":\"false\");outside=outside||bad});\nconst cement=x[0],blast_furnace_slag=x[1],fly_ash=x[2],water=x[3],superplasticizer=x[4],coarse_aggregate=x[5],fine_aggregate=x[6];\nconst scaled_cement = (cement-265.4440002)\/104.6699982;\nconst scaled_blast_furnace_slag = (blast_furnace_slag-86.28520203)\/87.82649994;\nconst scaled_fly_ash = (fly_ash-62.79529953)\/66.22769928;\nconst scaled_water = (water-183.0599976)\/19.32859993;\nconst scaled_superplasticizer = (superplasticizer-6.995759964)\/5.392280102;\nconst scaled_coarse_aggregate = (coarse_aggregate-956.059021)\/83.8015976;\nconst scaled_fine_aggregate = (fine_aggregate-764.3770142)\/73.12049866;\nconst dense_layer_1_output_0 = Math.tanh( 0.23991552 + (0.6390509009*scaled_cement) + (0.05165676028*scaled_blast_furnace_slag) + (0.09407510608*scaled_fly_ash) + (0.2738842368*scaled_water) + (0.06413187087*scaled_superplasticizer) + (0.1082144454*scaled_coarse_aggregate) + (0.08382763714*scaled_fine_aggregate) );\nconst dense_layer_1_output_1 = Math.tanh( 0.2461378276 + (0.0672128275*scaled_cement) + (0.6354727149*scaled_blast_furnace_slag) + (0.2852489054*scaled_fly_ash) + (-0.2807556987*scaled_water) + (-0.600235939*scaled_superplasticizer) + (0.07180905342*scaled_coarse_aggregate) + (-0.102127865*scaled_fine_aggregate) );\nconst dense_layer_1_output_2 = Math.tanh( 0.0345897153 + (0.2447437048*scaled_cement) + (0.1172790676*scaled_blast_furnace_slag) + (-0.02495710179*scaled_fly_ash) + (0.176685974*scaled_water) + (0.05444622412*scaled_superplasticizer) + (0.007595078554*scaled_coarse_aggregate) + (-0.02013533376*scaled_fine_aggregate) );\nconst dense_layer_1_output_3 = Math.tanh( -0.4845633507 + (0.4642761052*scaled_cement) + (0.2746075094*scaled_blast_furnace_slag) + (0.2328586727*scaled_fly_ash) + (-0.506093204*scaled_water) + (0.08450572193*scaled_superplasticizer) + (0.2600531876*scaled_coarse_aggregate) + (0.3445454538*scaled_fine_aggregate) );\nconst dense_layer_1_output_4 = Math.tanh( -0.1198322698 + (-0.5154670477*scaled_cement) + (-0.09656532109*scaled_blast_furnace_slag) + (-0.02129566297*scaled_fly_ash) + (0.1568460613*scaled_water) + (-0.4756751359*scaled_superplasticizer) + (0.1347480118*scaled_coarse_aggregate) + (-0.2773837745*scaled_fine_aggregate) );\nconst dense_layer_1_output_5 = Math.tanh( -0.01274420321 + (-0.1911222488*scaled_cement) + (-0.1406879425*scaled_blast_furnace_slag) + (0.01794729754*scaled_fly_ash) + (-0.1754956096*scaled_water) + (-0.05975896493*scaled_superplasticizer) + (-0.03427903727*scaled_coarse_aggregate) + (0.01569590531*scaled_fine_aggregate) );\nconst dense_layer_1_output_6 = Math.tanh( 0.03754479066 + (0.1152876839*scaled_cement) + (0.2272853106*scaled_blast_furnace_slag) + (0.04614272341*scaled_fly_ash) + (0.2128768563*scaled_water) + (0.1501749158*scaled_superplasticizer) + (0.02542976663*scaled_coarse_aggregate) + (-0.09971896559*scaled_fine_aggregate) );\nconst dense_layer_1_output_7 = Math.tanh( 8.272414561e-06 + (-0.103595525*scaled_cement) + (-0.09436781704*scaled_blast_furnace_slag) + (0.04183280468*scaled_fly_ash) + (-0.08057995886*scaled_water) + (-0.03536500037*scaled_superplasticizer) + (-0.005719161592*scaled_coarse_aggregate) + (0.01351742353*scaled_fine_aggregate) );\nconst dense_layer_1_output_8 = Math.tanh( 0.00238332618 + (-0.1500693262*scaled_cement) + (-0.138360709*scaled_blast_furnace_slag) + (0.03680619225*scaled_fly_ash) + (-0.1362865865*scaled_water) + (-0.05458300933*scaled_superplasticizer) + (-0.025834525*scaled_coarse_aggregate) + (0.01792443916*scaled_fine_aggregate) );\nconst dense_layer_1_output_9 = Math.tanh( 0.001253336435 + (0.0517296046*scaled_cement) + (0.03924541175*scaled_blast_furnace_slag) + (-0.01719077304*scaled_fly_ash) + (0.03905789182*scaled_water) + (0.01685577258*scaled_superplasticizer) + (-0.005136854481*scaled_coarse_aggregate) + (-0.005990707316*scaled_fine_aggregate) );\nconst approximation_layer_output_0 = ( -0.01981435716 + (0.7498760819*dense_layer_1_output_0) + (0.7791118622*dense_layer_1_output_1) + (0.3367168307*dense_layer_1_output_2) + (0.8731762767*dense_layer_1_output_3) + (-0.6954141259*dense_layer_1_output_4) + (-0.3202452362*dense_layer_1_output_5) + (0.4023092389*dense_layer_1_output_6) + (-0.1742091477*dense_layer_1_output_7) + (-0.2709912956*dense_layer_1_output_8) + (0.07161010057*dense_layer_1_output_9) );\nconst unscaling_layer_output_0=approximation_layer_output_0*14.71107674+36.74861145;\nconst compressive_strength = unscaling_layer_output_0;\nconst total=x.reduce((a,b)=>a+b,0),jointOutside=total<2194.6||total>2551.0;\ndocument.getElementById(\"cp-output\").textContent=compressive_strength.toFixed(2)+\" MPa\";\ndocument.getElementById(\"cp-total\").textContent=total.toFixed(1)+\" kg\/m\u00b3\";\ndocument.getElementById(\"cp-status\").textContent=outside?\"Warning: at least one input is outside the training range; this prediction should not be trusted.\":jointOutside?\"Caution: every input is individually in range, but total constituent mass is outside the 2,194.6\u20132,551.0 kg\/m\u00b3 observed interval.\":\"All inputs and total constituent mass are within the observed ranges. Joint feasibility and engineering requirements still need review.\";\n}\nfunction loadPreset(){const values=presets[document.getElementById(\"cp-preset\").value];ids.forEach((id,i)=>document.getElementById(id).value=values[i]);calculate()}\nform.addEventListener(\"submit\",e=>{e.preventDefault();calculate()});\ndocument.getElementById(\"cp-load\").addEventListener(\"click\",loadPreset);\nloadPreset();\n})();\n<\/script>\n<h3>Download and reproduce<\/h3><div class=\"ndb-downloads\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-properties-python-model-2026.zip\">Download Python model (ZIP)<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/07\/concrete-properties-project-optimization-cases-2026.zip\">Download Neural Designer project + CSV (ZIP)<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/10\/concrete_properties.csv\">Download concrete_properties.csv<\/a><\/div>\n<h3>Video walkthrough<\/h3><p>The original tutorial remains available as a step-by-step Neural Designer walkthrough. Numerical results on this page reflect the newly exported project.<\/p><div class=\"ndb-video\"><iframe src=\"https:\/\/www.youtube.com\/embed\/LfjDgILVPZ8\" title=\"Concrete compressive strength Neural Designer tutorial\" loading=\"lazy\" allowfullscreen><\/iframe><\/div><\/section>\n\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Scope and limitations<\/h2><ul>\n<li>The model represents 28-day strength only. It cannot estimate early-age or long-term strength because age is fixed and absent from the inputs.<\/li>\n<li>The data comes from a public laboratory collection and does not establish transfer to a specific plant, cement source, aggregate grading, admixture product, curing regime or test procedure.<\/li>\n<li>Random row splitting and duplicated formulations can produce a more optimistic test than validation by unseen formulation family, production batch or chronological campaign.<\/li>\n<li>The target is compressive strength only; workability, slump, density, durability, heat, shrinkage, cost and embodied carbon are not modelled.<\/li>\n<li>Inputs are constituent masses, but the model does not enforce volume balance, total yield, water-to-binder limits, aggregate proportions or material compatibility.<\/li>\n<li>The observed range of each variable does not define a valid multidimensional mixture domain. Joint feasibility must be checked separately.<\/li>\n<li>Production deployment requires local data, grouped or time-aware validation, drift monitoring, uncertainty limits and periodic recalibration.<\/li>\n<li>Predictions support screening and experimentation; qualified personnel and standards-based testing remain responsible for mix approval and structural acceptance.<\/li><\/ul><\/section>\n\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li>Yeh, I.-C. <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/165\/concrete%2B\">Concrete Compressive Strength Dataset<\/a>, UCI Machine Learning Repository, DOI: <a href=\"https:\/\/doi.org\/10.24432\/C5PK67\">10.24432\/C5PK67<\/a>.<\/li><li>Yeh, I.-C. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0008884698001653\">Modeling of strength of high-performance concrete using artificial neural networks<\/a>, Cement and Concrete Research 28(12), 1797\u20131808 (1998).<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\">Neural Designer testing analysis<\/a>: goodness-of-fit and regression metrics.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/\">Neural Designer model deployment<\/a>: directional outputs, response optimization and model export.<\/li><\/ul><\/section>\n<\/div><\/div>","protected":false},"author":13,"featured_media":2414,"template":"","categories":[29],"tags":[40,43],"class_list":["post-3478","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-chemistry","tag-industry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Model concrete properties using machine learning<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to design concrete mixtures with specified properties and reduced costs, based on laboratory tests.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/concrete-properties-assesment\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Concrete properties machine learning example\" \/>\n<meta property=\"og:description\" content=\"Compressive strength is one of the most important properties of concrete. 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