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GP-CV-0IRA1EECivilReady

Fire Safety and Structural Resilience of Mass-Timber Buildings in Warm Climates

A civil engineering study of published timber-fire experiments, delayed internal heating, structural evidence and warm-climate moisture.

Fire Safety and Structural Resilience of Mass-Timber Buildings in Warm Climates project visual
GP-CV-0IRA1EE · Civil
  • Python 3.12
  • NumPy
  • Pandas
  • Matplotlib

Software compatibility

Python 3.12 or later

Includes compact published-data extracts and an offline Docker workflow. No paid fire-modelling software or confidential building data is required.

Project definition

Problem statement

Exposed timber can change fire development, but one peak temperature or heat-release number cannot describe the complete response.

The civil engineering question is how compartment-fire evidence connects to heated structural behaviour, cooling, connections and construction moisture without overstating what the experiments prove.

Project objectives

  • Reanalyse six published NIST compartment-fire records with traceable source data.
  • Compare common time windows, flashover alignment and peak averaging.
  • Verify heat-release integrals and explain differences between peak and cumulative measures.
  • Use dimensionless theory to investigate delayed internal heating and section geometry.
  • Connect fire evidence with warm-climate moisture and structural resilience literature.

Project structure

Project components

01

Published experiments

Retains source hashes, extracted heat-release measurements and operator event times for six different configurations.

02

Data analysis

Computes energy integrals, common and aligned time windows, peak means and adjacent interval totals.

03

Thermal theory

Demonstrates heat diffusion and delayed internal peaks using dimensionless analytical solutions.

04

Structural evidence

Reviews heated flexure, connections, delamination, extinction and cooling-phase stability.

05

Warm-climate interfaces

Examines construction exposure, drying, moisture monitoring and envelope details.

Methodology

Project workflow

  1. 01
    Check the sources

    Review each experiment, the data manifest and the limitations of the published measurements.

  2. 02
    Run the comparisons

    Generate the retained heat-release, time-window and peak-sensitivity results.

  3. 03
    Explore the theory

    Examine dimensionless temperature histories and independent numerical checks.

  4. 04
    Interpret the evidence

    Connect the numerical findings to material, connection and warm-climate literature.

  5. 05
    Extend the study

    Choose an explicitly defined research question and identify the additional data or testing it needs.

Demonstration scenario

Test 1-6 has the highest first-hour energy, while test 1-3 has the highest 60-sample mean peak. Changing the observation window changes some comparisons, showing why a ranked result needs a clearly stated measure and time period.

Engineering

Tools and method

Tools
The project uses Python 3.12, NumPy, Pandas, Matplotlib for subject analysis, simulation, and results.
Calculations
Python, NumPy and Pandas retain signed measurements, consistent units and explicit observation windows.
Figures
Matplotlib generates measured-data comparisons and analytical theory figures.
Verification
Automated tests cover source integrity, integrals, boundary cases, equations and displayed values. Docker reproduces the workflow offline.
Documentation
The report includes introduction, literature review, theory, methodology, results, discussion, limitations, conclusions, further work and annotated references.

Testing

Evaluation

Evaluation measures

  • Agreement of generated tables with independently recomputed values
  • Sensitivity to comparison windows and peak averaging
  • Additivity of adjacent heat-release integrals
  • Thermal boundary conditions, stationary peaks and grid accuracy
  • Traceability and applicability of the reviewed literature

Project boundaries

  • The six configurations are different experiments, not statistical replicates or a controlled estimate of one isolated effect.
  • Published calorimeter measurements represent total heat release, not the timber contribution alone.
  • Dimensionless theory does not predict a real timber member capacity or a fire-resistance rating.
  • The optional strategy scores are author assumptions, separate from measured findings.
  • No building approval, code compliance, construction specification or guarantee of safety is provided.
  • No information collected.

Included

  1. 01Six published NIST fire experiments with source hashes and event records
  2. 02Energy-window, peak-averaging and interval comparisons
  3. 03Dimensionless thermal diffusion and section-geometry examples
  4. 0472-page project documentation in PDF and editable Word formats
  5. 0513-page setup and usage guide in PDF and editable Word formats
  6. 0617 annotated references and a source matrix
  7. 07Nine report figures, five tables and two sourced literature photographs
  8. 08Reproducible Python analysis, retained results and 50 automated tests
  9. 09Offline Docker workflow and a separately labelled optional comparison exercise
  10. 10Project ZIP download and private repository access

Project record

No information is collected on this page.

Permanent project ID
GP-CV-0IRA1EE
Catalogued
21 Aug 2026
Completed
05 Sept 2026
Verified
05 Sept 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.