News Archives - Professor Hamzeh Hajiloo Research

New Fire Modeling App for Compartment Fire Analysis

hamzehhajiloo — Fri, 12 Sep 2025 17:26:09 +0000

A new software application is now available to generate realistic compartment fire scenarios quickly and accurately. The app uses a simplified heat release rate (HRR)–time model derived from large-scale fire data and automates the creation of both HRR curves and temperature–time histories.

Key properties of the app:

Generates HRR and temperature–time curves based on fuel load, ventilation, and compartment geometry.
Compatible with deterministic and probabilistic analysis, allowing thousands of fire scenarios to be simulated efficiently.
Produces results validated against large-scale fire tests, with reliable predictions of peak compartment temperatures.
Provides distributions of critical parameters such as flashover time, maximum HRR, and duration above key temperature thresholds.
Designed for easy use in performance-based and risk-informed fire safety engineering.

The app is freely accessible at It offers a practical tool to support fire design by balancing accuracy, transparency, and wide applicability beyond the limits of existing parametric fire models

GUI for capturing the full load-deflection response of FRCM-strengthened beams

hamzehhajiloo — Sat, 28 Dec 2024 22:05:57 +0000

A graphical user interface (GUI) was used in the MATLAB environment. However, the application needs MATLAB Runtime which is a standalone set of shared libraries that enables the execution of compiled MATLAB codes. This can be considered as the limitation of the developed user-friendly application. The main user interface is shown below. The application is able to capture the full load-deflection curve of the strengthened beam using the mechanical and geometrical properties of RC beam and FRCM. Additionally, it calculates stiffness and absorbed energy.

Download the application .

Structural Damage Level of Road Bridges In Fire

MAJIDHAMIDIIRAVANI — Mon, 17 Apr 2023 03:03:37 +0000

Creating software that is easy for users to navigate and incorporates the findings of an artificial neural network is a viable approach for applying the benefits of machine learning in practical situations. Machine learning-based software can provide a more user-friendly interface, allowing users without extensive knowledge of machine learning to leverage its power. Moreover, ML-based software can facilitate the deployment of machine learning models in real-world scenarios, making it easier to integrate with other systems and technologies. The approach of practical implementation of ML in real problems enables engineers to obtain precise results from ANN without having to conduct numerous experiments or perform complicated mathematical calculations. The proposed ML-based software in this study was developed based on C# which is a general-purpose, high-level programming language supporting multiple paradigms. Users can obtain the estimated damage level in a bridge based on the location, material, structural system of a bridge, AADT, ignition source, combustible type, and face of a bridge to which fire is exposed.