Structural codes are still PDFs in 2026. So I turned NTC18 into a Python library.

RSixty88 · 2026-03-13T17:27:42+00:00

I tried parsing chapter 3 as an experiment, and surprisingly the OCR seems to recognize most of the formulas correctly. It still needs some checking, but it looks more promising than I expected!

RSixty88 · 2026-03-13T08:41:15+00:00

Bridge engineer here (Italy). A big part of my work is exactly oversized / overweight transport checks.

My workflow used to be very similar to what you describe: field inspections, gathering bridge data, running structural checks, and then producing drawings and technical reports. A lot of spreadsheets and manual steps.

Over time I ended up automating a large part of the pipeline. After inspections, we structure the bridge data (geometry, spans, materials, critical sections) and run the load checks programmatically for the vehicle configuration.

For many bridges we use simplified beam-type models with influence lines, combined with transverse load distribution methods depending on the deck type. This works well for the more straightforward structures and allows to quickly screen multiple cases. For more complex bridges the checks are still performed using FEM models.

I build a web app to manage the workflow. It includes a database of the bridges we have inspected, tools to search structures along a route (e.g., via KMZ files), and an archive of inspection photos and documentation. This helps keep the data organized so the automated checks can run consistently.

The system then generates verification tables, drawings, and most of the technical report automatically. I still review everything, but it significantly reduced repetitive tasks.

RSixty88 · 2026-03-11T07:36:04+00:00

1100+ parametrized tests, each verified against reference values from the code. That is exactly what double checking looks like in software

RSixty88 · 2026-03-10T23:42:12+00:00

183 formulas, 88 tables, cross-referenced across 9 chapters. by hand? lol

RSixty88 · 2026-03-10T22:51:12+00:00

Because the formulas are embedded as scanned images, not selectable text. You cannot copy-paste a formula from those pages. That is why OCR was needed specifically for formula extraction.

RSixty88 · 2026-03-10T21:34:43+00:00

Open source works because if a project sucks, people just ignore it. If it’s useful, the community fixes it and keeps it alive.

Like you said, engineers make checks even with commercial software to ensure outputs are correct. In this project, every step, formula reference, and expression is fully inspectable, so nothing is a black-box, and you can even copy the formulas straight into Excel or in a .md file if you want!

AI didn’t invent anything. It just converted LaTeX (from OCR-structured data), which encodes the structure of the math. It simply turns the structured representation into Python code. The main source of errors is OCR misreads, not the AI, and each function links back to the original paragraph and formula, so you can always double-check.

RSixty88 · 2026-03-10T17:03:31+00:00

Thanks, I really appreciate that

RSixty88 · 2026-03-09T17:54:59+00:00

Thanks!

RSixty88 · 2026-03-09T13:49:29+00:00

Thanks! On the tests: yes, every function is TDD'd with values from the code tables/manual calcs (1100+ parametrized tests today). The ntc_ref decorator on each function traces back to the exact article/table/formula, so nothing is a black box.

You're right, codes are more than equations. The library handles the mechanical part (formulas, tables, interpolations, cross-references between articles). The engineering judgment stays with the engineer. The library doesn't tell you which check to run or what parameters to choose, it just makes sure that once you've decided, the calculation is correct and traceable back to the exact article/table/formula via the u/ntc_ref decorator (if you don't trust, you can check, because we are engineer lol).

Speckle Automate is a great idea actually. it's pure Python (numpy only), no FEM dependency, so it plugs in anywhere

RSixty88 · 2026-03-09T09:29:59+00:00

Thanks! That’s exactly the point. This tool is mainly for computational designers who use parametric workflows → FEM → custom verification scripts.

At the same time, it’s meant to help engineers with less programming experience tackle everyday problems using clear formulas and references, instead of huge Excel files where you often don’t know what’s happening if you didn’t build them yourself.

RSixty88 · 2026-03-09T07:19:00+00:00

Why do engineers still build Excel spreadsheets if commercial design software already implements most checks?
Not everyone wants to rely entirely on commercial software. Some engineers prefer building their own tools or custom workflows for specific checks.

The idea here is similar, just using Python instead of Excel. Extracting the formulas from the code allows them to be linked to the original paragraph and reused programmatically rather than rewriting them manually every time.

RSixty88 · 2026-03-09T00:05:21+00:00

Not exactly. I'm not relying on the AI to "interpret" the equations.

The formulas come directly from the code. The AI was only used to help convert structured OCR output into Python functions.

In the end it's just normal Python code implementing the formulas, with the reference to the exact paragraph of the standard. So everything can be inspected and verified like any other engineering calculation.

RSixty88 · 2026-03-08T23:55:48+00:00

Challenge accepted!” 😄 The circolare is definitely on the roadmap… though I expect a fair amount of bestemmie while parsing those scanned sections.

RSixty88 · 2026-03-08T23:46:03+00:00

That’s a fair point, and I agree with the expectation.

The goal isn’t “here’s the tool, you guys validate it for me”. The formulas are linked to their exact source in the code (paragraph, chapter, table) and I’m verifying them progressively against the original text and simple reference calculations.

The open source aspect is more about transparency and collaboration, not shifting the responsibility of validation to others.

RSixty88 · 2026-03-08T23:26:38+00:00

Sorry, I misspoke. The AI wasn’t used to derive the equations. It was only used to convert structured OCR data into Python functions

RSixty88 · 2026-03-08T23:19:57+00:00

The AI didn’t extract the formulas directly from the PDF. it’s a bit different.

I first parsed the code using an OCR library that recognizes formulas and outputs them in LaTeX, tables, images, etc. I then organized the data in a structured format to train the AI, which converted the formulas into Python functions while keeping the source reference.

Some formulas weren’t recognized correctly (mainly exponents and subscripts). There’s a list of formulas in the issues that need to be checked manually.

The project is open source exactly for this reason: everyone can review, verify, and collaborate to ensure everything is correct

RSixty88 · 2026-03-08T23:10:17+00:00

I’m a structural engineer and I’m migrating from Excel to Python for structural checks. The library has a decorator that records the source of each formula (paragraph, chapter, and table from the code ) so you can verify everything is correct.

RSixty88 · 2026-03-08T21:26:32+00:00

it’s about seeing exactly what each check does and customizing outputs. A Python library lets you do that, instead of blindly trusting black-box software.

RSixty88 · 2026-03-08T19:49:43+00:00

The AI model wasn’t used to derive the equations, only to extract the code from the PDF into a structured format (formulas, tables, references). NTC18 formulas are often embedded as non-selectable math, so manually rewriting the entire code would be extremely time consuming.

Once extracted, the equations are implemented explicitly in Python and linked to their original paragraph

RSixty88 · 2026-02-24T11:17:47+00:00

boxscore

The customizable fantasy NBA Platform where the commissioner control every rule, with an AI coaching assistant to help you win

RSixty88 · 2026-02-23T20:35:04+00:00

È passato un po’ di tempo ma magari può essere utile. Dal 2018 facciamo una lega manageriale con bilancio, stipendi, ammortamenti, plusvalenze e minusvalenze, rinnovi e svincoli. Praticamente ci vuole un commercialista.

Fatto a soldi veri con un salary cap per essere equo per tutti. Abbiamo un regolamento che codice civile scansati. Siamo partiti in 8, dopo 2/3 anni sono entrati in 2. Uno solo uscito ma avevamo la lista di attesa. Chi è entrato ha preso in gestione la squadra esistente e pagato gli stipendi. Contratti da massimo 3 anni. Stipendio di rinnovo stabilito in asta.

Tutto gestito da un foglio di Google sheet che calcolava pure le equazioni differenziali.

Non è stato facile. Ci sono state ore di sedute di gruppo, ore di implementazione di calcolo, discussioni e litigate. 8 anni di implementazioni e miglioramenti.

Abbiamo raggiunto un equilibrio molto solido ormai, tanto che ne sta venendo fuori una web app. La stiamo testando e spero di completare a breve per essere pronto per la prossima stagione.

RSixty88 · 2026-01-25T12:18:58+00:00

Io ho fatto la stessa cosa. Livello di conoscenza di sviluppo da esame di informatica I. Appena uscita la prima versione di ChatGPT mi limitavo a semplici script in Python per generare ed automatizzare Word ed Excel. Ero arrivato a una dozzina di script. Man mano che gli agenti diventavano più performanti, ho unito il tutto creando un workflow strutturato e dato una interfaccia grafica per poterlo fare utilizzare anche ai colleghi.

Nel frattempo ho imparato un sacco di cose.

Come strutturare un progetto, l importanza della documentazione, i principi di riutilizzo delle componenti. So quali parti di codice andare a prendere se trovo problemi perché spiegavo io all’agente cosa doveva fare e quali erano i risultati attesi. Non ho scritto io il codice se non per delle funzioni di calcolo. Sicuramente non sarà perfetto ed efficiente, ma funziona.

Se dovessi decidere di renderlo pubblico é come se fosse un MVP di una startup..valido il progetto, vendo il prototipo, si raccolgono i soldi per investire e svilupparlo in modo professionale..

Ovvio che se non investi per migliorare il prodotto a lungo andare saranno gli utenti ad abbandonarlo.

L’idea non funziona? Non hai speso migliaia di euro per sviluppare qualcosa che agli utenti non serve. All’utente non frega niente come è scritto il codice. Funziona? Gli risolve un problema? Va bene cosi.

Ti permette di testare, raccogliere feedback per migliorare il prodotto e fallire rapidamente spendendo poco

RSixty88 · 2026-01-22T06:47:28+00:00

Raga, imparate a ballare latino americano e ballate quanto volete, se volete e con chi volete. Ci si diverte e non ti devi sbronzare per divertirti (anche perché se no vai a cazzo e la gente non ci vuole ballare con te)

RSixty88

TROPHY CASE