Mohammad Khaled al-Bashiti

• Doctor of Engineering, PhD student
• Research Assistant at Clemson University

In the rapidly evolving optimization and metaheuristics domains, the efficacy of algorithms is crucially determined by the benchmark (test) functions. While several functions have been developed and derived over the past decades, little information is available on the mathematical and visual description, range of suitability, and applications of ma...

Using an extensive database, a sensitivity analysis across fifteen machine learning (ML) classifiers was conducted to evaluate the impact of various data manipulation techniques, evaluation metrics, and explainability tools. The results of this sensitivity analysis reveal that the examined models can achieve an accuracy ranging from 72-93% in predi...

The field of machine learning (ML) has witnessed significant advancements in recent years. However, many existing algorithms lack interpretability and struggle with high-dimensional and imbalanced data. This paper proposes SPINEX, a novel similarity-based interpretable neighbor exploration algorithm designed to address these limitations. This algor...

What can we learn from over 1000 tests on fire-induced spalling of concrete? A statistical investigation of critical factors and unexplored research space." Construction and Building Materials. Abstract This paper presents a comprehensive statistical investigation of the largest database on fire-induced spalling of concrete collected to date. In to...

Assessing the ability of reinforced concrete (RC) columns to withstand the effects of fire is a multifaceted and intricate problem due to the various factors that influence their fire response. As such, engineers may find it challenging to precisely predict such fire resistance. While some codal provisions exist and fire testing/advanced modeling c...

The field of machine learning (ML) has witnessed significant advancements in recent years. However, many existing algorithms lack interpretability and struggle with high-dimensional and imbalanced data. This paper proposes SPINEX, a novel similarity-based interpretable neighbor exploration algorithm designed to address these limitations. This algor...

Concrete, encountering harsh conditions such as fire, is prone to damage. One of the most critical ones is spalling, and this tragic event continues to be a challenging area of research. A thorough examination of the available literature reveals the difficulty of anticipating spalling. As a result, this work proposes a nomogram as a tool to predict...

This paper presents preliminary results to describe the fire-induced spalling of concrete using explainable artificial intelligence (XAI). One thousand fire tests were collected from the literature consisting of twenty-two different mechanical, environmental, material, and geometrical parameters, creating the largest spalling database (up to date)....

This paper adopts eXplainable Artificial Intelligence (XAI) to identify the key factors influencing fire-induced spalling of concrete and to extract new insights into the phenomenon of spalling by investigating over 640 fire tests. In this pursuit, an XAI model was developed, validated, and then augmented with two explainability measures, namely, S...

Whether triggered by natural or human-made events, wildfires are considered one of the most traumatic events to our community and environment. Thus, properly predicting wildfires continues to be an active area of research. This work showcases a statistical overview of the problem of wildfires and then presents a dense data-driven (D³) approach that...