M.Z. Naser

M.Z. Naser
Clemson University | CU · Department of Civil Engineering/AI Research Institute for Science & Engineering

Ph.D., P.E.

About

158
Publications
54,457
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
2,350
Citations
Citations since 2016
112 Research Items
2154 Citations
20162017201820192020202120220200400600
20162017201820192020202120220200400600
20162017201820192020202120220200400600
20162017201820192020202120220200400600
Introduction
M. Z. Naser is an Assistant Professor of Civil Engineering/AI Research Institute for Science & Engineering. My research interests cover the areas of Causal and XAI and machine learning, Structural fire engineering, and understanding the overall Behavior of structures in extreme conditions. He is currently serving as a committee member in ACI committees (133, 216 and 447) as well as PCI fire committee and ASCE Fire Protection Committee.  www.mznaser.com
Additional affiliations
January 2019 - present
Clemson University
Position
  • Professor (Assistant)
September 2016 - present
Michigan State University
Position
  • PostDoc Position
August 2011 - August 2016
Michigan State University
Position
  • Research Assistant
Education
August 2011 - August 2016
Michigan State University
Field of study
  • Structural fire engineering

Publications

Publications (158)
Article
Full-text available
Prestressed steel-concrete composite beams with concreted and corrugated webs are a novel system that have been attracting attention lately. Unfortunately, there has been little research with regard to the fire performance of such beams. To overcome this knowledge gap and in the hope of exploring attractive solutions to improve the fire resistance...
Article
Full-text available
13 Probabilistic approaches provide a more realistic look into assessing structures under fire 14 conditions and overcome some limitations observed in the more traditional (deterministic) 15 approaches. These approaches have also been introduced to the fire engineering domain, 16 e.g., fire probabilistic risk analysis and probabilistic structural f...
Article
Full-text available
Causality is the science of cause and effect. It is through causality that explanations can be derived, theories can be formed, and new knowledge can be discovered. This paper presents a modern look into establishing causality within structural engineering systems. In this pursuit, this paper starts with a gentle introduction to causality. Then, th...
Book
Full-text available
Artificial Intelligence (AI) continues to transform our lives on a daily basis. Advancements on the AI front can only be described as lightning-fast, especially when compared to what we are accustomed to in our civil and environmental engineering (CEE) discipline, one of the most classical and perhaps oldest engineering disciplines. Such rapid adva...
Article
Full-text available
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...
Article
Full-text available
The rapid advancement in computer vision has facilitated new means for the automatic assessment of structural damages. This study aims to develop a deep learning-based autonomous damage detection framework for concrete structures under fire conditions. A hybrid deep learning network comprising of Convolution Neural Network (CNN) and Long Short Term...
Article
Full-text available
Self-Compacting Concrete (SCC) is a green construction material widely used in the construction industry. While normal weight concrete has notable performance under fire conditions, the open literature continues to lack works examining the structural fire performance of SCC elements. Intending to bridge this knowledge gap, the present study aims to...
Article
Full-text available
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...
Article
Full-text available
Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are emerging techniques capable of delivering elegant and affordable solutions which can surpass those obtained through traditional methods. Despite the recent and rapid advancements in developing next-gen AI-based techniques, we continue to lack a systemic understanding of...
Article
Full-text available
Fire-induced spalling of concrete continues to be an intriguing and intricate research problem. A deep dive into the open literature highlights the alarming discrepancy and inconsistency of existing theories, as well as the complexity of predicting spalling. This brings new challenges to creating fire-safe concretes and primes an opportunity to exp...
Book
Full-text available
This handbook aims at modernizing the current state of civil engineering and firefighting, especially in this era where infrastructures are reaching new heights, serving diverse populations, and being challenged by unique threats. Its aim is to set the stage toward realizing contemporary, smart, and resilient infrastructure. The Handbook of Cognit...
Article
Full-text available
Due to the superior properties of concrete, structural members made of concrete often satisfy fire requirements specified in codes and standards without special installations or the use of external insulation. A closer examination into fire codal provisions shows that they are primarily founded for new constructions or that which does not suffer fr...
Article
Full-text available
This paper introduces CLEMSON, an automated machine learning (AutoML) virtual assistant (VA) that enables engineers to carry acCeLErated, siMulation-free, tranSparent, reduced-Order, and infereNce-based fire resistance analysis with ease. This VA learns from physical observations taken from real fire tests to bypass bottlenecks and ab initio calcul...
Article
Full-text available
Masonry is an inert construction material with favorable thermal and mechanical properties. While masonry is widely used in buildings, the fire performance of this material has not received much attention over the years. This continues to hinder the understanding of the fire behavior of masonry. To bridge this knowledge gap, this study presents the...
Article
Full-text available
This paper presents a framework for integrating Explainable and Anomalous Machine Learning (EAML) into a digital twin to enable finetuning of mixtures as a mean to realize next-gen concretes with favorable performance. In this framework, both anomalous unsupervised and explainable supervised ML algorithms are joined to create a virtual assistant ca...
Article
Full-text available
The past few years have witnessed the rise of an era that establishes Artificial Intelligence (AI) as a new frontier within the civil engineering domain. A closer look into tremors arising from our academia and industry infer a general interest in AI; however, this interest is faced with a series of serious questions: Is academia or the civil engin...
Article
Full-text available
Ultra-high-performance concrete (UHPC) has superior strength and durability, and hence it has been primarily favored in a variety of applications in structural engineering. While the open literature presents a series of predictive machine learning (ML) models to predict the strength of UHPC from its constituent materials, the reverse problem of ide...
Article
Full-text available
The failure mode of fiber-reinforced polymers reinforced concrete (FRP-RC) beams is a concern in the capacity evaluation. Therefore, developing a robust method to identify the failure mode of FRP-RC beams is warranted. This paper proposes a support vector machine (SVM) algorithm, together with comprehensive compiled experimental databases and a val...
Article
Full-text available
The role of machine learning (ML) continues to rise in the structural fire engineering area. Noting the widespread of supervised ML approaches, such methods are being heavily utilized nowadays. On the other hand, little interest has been dedicated to unsupervised ML. Unlike supervised learning, unsupervised learning algorithms are trained using dat...
Preprint
Full-text available
Experiments remain the gold standard to establish an understanding of fire-related phenomena. A primary goal in designing tests is to uncover the data generating process (i.e., the how and why the observations we see come to be); or simply what causes such observations. Uncovering such a process not only advances our knowledge but also provides us...
Preprint
Full-text available
Much of our experiments are designed to uncover the cause(s) and effect(s) behind a data generating mechanism (i.e., phenomenon) we happen to be interested in. Uncovering such relationships allows us to identify the true working of a phenomenon and, most importantly, articulate a model that may enable us to further explore the phenomenon on hand an...
Article
Full-text available
People with disabilities are one of the most vulnerable groups involved in building fires. According to U.S. Fire Administration, an estimated 700 home fires involve people with physical disabilities each year, while over 1700 involve those with mental health disorders. Despite this, there is very little evacuation research for the disabled populat...
Article
Full-text available
Artificial intelligence (AI) has been established as a universal language for solving science and engineering problems. Despite the rise of big data, the success of AI in parallel fields, and exciting works published at this frontier, some in the civil engineering community tie AI to a mystique stigma. And yet, there is also ever-growing inertia to...
Article
Full-text available
With the advent rise of automation, it is now possible to trace and detect damage in structural systems with ease. Unfortunately, existing inspection methods continue to suffer on a number of fronts; i.e., laborious, costly, and mediocre performance. In order to bridge this knowledge gap, this work aims to develop an autonomous and open-source deep...
Article
This paper presents an experimental study on the flexural behavior of reinforced concrete (RC) beams externally strengthened with a unique and sustainable type of fiber reinforced polymer (FRP), named polyethylene tere-phthalate (PET). Compared to conventional FRPs, PET-FRP is characterized by its large rupture strain and low elastic modulus. The f...
Article
Full-text available
Artificial intelligence (AI) is paving the way towards Fire 4.0, and the next few years will be elemental to how this technology transforms our academia, practice, and entrepreneurship. Despite the growing interest between fire researchers and practitioners, AI remains absent from our curriculum and we continue to lack a methodical framework to ado...
Article
Full-text available
This paper aims at leveraging recent advancements in interpretable machine learning to better understand how anthropometric measurements can be tied to body mass index (BMI). Two objectives are of interest to this work, the first is to develop a properly validated interpretable machine learning (ML) ensemble capable of accurately predicting BMI, an...
Article
Full-text available
Masonry has superior fire resistance properties stemming from its inert characteristics, and slow degradation of mechanical properties. However, once exposed to fire conditions, masonry undergoes a series of physio-chemical changes. Such changes are often described via temperature-dependent material models. Despite calls for standardization of such...
Article
Full-text available
This paper presents the development of systematic machine learning (ML) approach to enable explainable and rapid assessment of fire resistance and fire-induced spalling of reinforced concrete (RC) columns. The developed approach comprises an ensemble of three novel ML algorithms namely; random forest (RF), extreme gradient boosted trees (ExGBT), an...
Article
Full-text available
This paper examines the fire performance of uninsulated and uncoated restrained steel-concrete composite beams supplemented with externally prestressed strands through advanced numerical simulation. In this work, a sequentially coupled thermo-mechanical analysis is carried out using ABAQUS. This analysis utilizes a highly nonlinear three-dimensiona...
Article
Full-text available
Building adaptation and re-use can contribute to a circular and sustainable built environment, as existing buildings are adapted and the need for new construction materials is reduced. The “adaptability” of buildings has been widely studied; however, few of these studies are quantitative. This paper uses Artificial Neural Networks (ANN) and Logisti...
Article
Full-text available
This study presents a machine learning (ML) approach to identify vulnerability of bridges to fire hazard. For developing this ML approach, data on a series of bridge fires was first collected and then analyzed through three algorithms; Random forest (RF), Support vector machine (SVM) and Generalize additive model (GAM), competing to yield the highe...
Article
Full-text available
Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily lives as well as complex and interdisciplinary fields. With the rise of commercial, open-source, and user-catered AI/ML tools, a key question often arises whenever AI...
Preprint
Full-text available
Artificial intelligence (AI) is paving the way towards the fourth industrial revolution with the fire domain (Fire 4.0). As a matter of fact, the next few years will be elemental to how this technology will shape our academia, practice, and entrepreneurship. Despite the growing interest between fire research groups, AI remains absent of our curricu...
Book
Full-text available
This special publication draws inspiration from the Technical Session entitled “The Concrete Industry in the Era of Artificial Intelligence,” held during the ACI Virtual Concrete Convention in spring 2020. To parallel the Technical Session, this special publication is also tailored to showcase the unprecedented potential of leveraging artificial in...
Chapter
This chapter examines structural fire engineering considerations that are specific to concrete, which is a common construction material. First, thermal and mechanical properties of concrete at elevated temperatures are discussed. Second, failure modes specific to concrete structures (e.g., explosive spalling) are examined. Lastly, pertinent analysi...
Article
Full-text available
Cold-formed steel (CFS) purlins and studs with staggered web perforations have been used in construction to improve the thermal efficiency of buildings. The perforations adversely affect structural properties of the members, especially their shear strength. Accurate shear strength predictions of such members is a challenging task, which is not easi...
Article
In this study, two machine learning (ML) algorithms including support vector regression (SVR) and artificial neural network (ANN) are employed to predict the ultimate strength of rectangular and circular concrete-filled cold-formed steel tubular (CFCFST) columns under concentric and eccentric loading. In total, 730 test results on CFCFST columns ar...
Article
Full-text available
While artificial intelligence (AI), and by extension machine learning (ML), continues to be adopted in parallel engineering disciplines, the integration of AI/ML into the structural engineering domain remains minutus. This resistance towards AI and ML primarily stems from two folds: 1) the fact that coding/programming is not a frequent element in s...
Article
Full-text available
Recent surveys have noted that the majority of bridges continue to serve for a prolonged period of time (+40 years) that far exceeds its intended operational lifespan. Given our limited resources to maintain and upkeep bridges, these structures become notoriously vulnerable to extreme events. Building upon the fact that bridges continue not to be s...
Preprint
Full-text available
This paper presents the development of systematic machine learning (ML) approach to enable explainable and rapid assessment of fire resistance and fire-induced spalling of reinforced concrete (RC) columns. The developed approach comprises of an ensemble of three novel ML algorithms namely; random forest (RF), extreme gradient boosted trees (ExGBT),...
Article
Full-text available
This paper presents; mapping functions, a machine learning (ML) and simulation-free approach to enable physics-guided and data-driven derivation of expressions that describe engineering phenomena. In this approach, a series of ML models are first developed to examine a given phenomenon, and insights from their analysis, together with those obtained...
Article
Full-text available
Machine learning (ML) continues to rise as an effective and affordable method of tackling engineering problems. Unlike other disciplines, the integration of ML into structural and fire engineering domains remains deficient. This is due in part to the lack of benchmark databases to compare the effectiveness of ML models. In order to bridge this know...
Article
Full-text available
Masonry, as a construction material, is known to perform well under elevated temperatures, which makes it an attractive choice for structural applications. This superior performance is a reflection of its inert thermal characteristics, good stability, and slow degradation of mechanical properties. Still, and similar to other construction materials,...
Article
Full-text available
Owing to their lower costs and functional properties, the construction industry has been increasingly adopting synthetic organic polymer (SOP) materials into linings, interiors and non-load bearing structural components. While SOPs have favourable properties and characteristics at ambient conditions, the same materials often perform poorly under mo...
Article
Full-text available
An experimental study is carried out to investigate the seismic performance of fire exposed reinforced concrete (RC) frame joints. This experimental program consisted of testing four full-scale joints used in RC frames. The first joint was tested at ambient conditions to serve as a benchmark case and to study room temperature response of concrete j...
Conference Paper
Full-text available
Space exploration and terraforming nearby planets have been fascinating concepts for the longest time. Nowadays, technological advancements in manufacturing, robotics, and propellants are thriving, it is only a matter of time before humans can start colonizing nearby moons and planets. In recognition of the 50th anniversary of the first manned luna...
Article
Full-text available
With limited resources to properly maintain and upgrade transportation infrastructure, bridges often end up exceeding their expected service lifespan; thus, becoming vulnerable to the adverse effects of aging and extreme loading conditions. In order to better assess the vulnerability of these structures, this study showcases the outcome of an obser...
Article
This paper reviews the fire problem in critical transportation infrastructures such as bridges and tunnels. The magnitude of the fire problem is illustrated, and the recent increase in fire problems in bridges and tunnels is highlighted. Recent research undertaken to address fire problems in transportation structures is reviewed, as well as critica...
Code
spAIng v1.0 is an AI-powered tool to predict fire-induced spalling in RC columns.
Article
Full-text available
Concrete-filled steel tubular (CFST) columns are unique structural members that capitalize on the synergy between steel and concrete materials. Due to complexities arising from the interaction between steel tube and concrete filling, the analysis and design of CFST columns are both intricate and tedious. A closer examination to the provisions of Am...
Article
Full-text available
Fiber-reinforced polymer (FRP) composites do not only possess superior mechanical properties, but can also be easy to tailor, install, and maintain. As such, FRPs offer novel and attractive solu-tions to facilitate strengthening and/or retrofitting of aging, weakened, and upgraded structures. Despite the availability of general code provisions, the...
Article
Full-text available
Fire is a chaotic and extreme phenomenon. While the past few years have witnessed the success of integrating machine intelligence (MI) to tackle equally complex problems in parallel fields, we continue to shy away from leveraging MI to study fire behavior or to evaluate fire performance of materials and structures. In order to advocate for the use...
Chapter
Full-text available
There is growing inertia to integrating modern technologies in disaster management, civil or structural engineering application. One such technology is that associated with data intelligent analytics, also known as machine learning commonly is artificial intelligence (AI). With the hope of highlighting positive integration of AI, this chapter prese...
Chapter
Full-text available
Once exposed to elevated temperatures, structural steel experiences physio-chemical reactions that mirrors material degradations. Such degradations are represented through temperature-dependent material models. Models, as valuable tool in fire analysis and design, can showcase how steel properties vary with rise in temperature. Since we continue to...
Article
Full-text available
Despite ongoing research efforts, we continue to fall short of arriving at a consistent representation of fire-induced spalling of concrete. This is often attributed to the complexity and randomness of spalling as well as our persistence in favoring traditional approaches as a sole mean to examine this phenomenon. With the hope of bridging this kno...
Conference Paper
Full-text available
Artificial intelligence (AI) is a computational technique that exploits hidden patterns between seemingly unrelated parameters to draw solution(s) to a given phenomenon. From this view, we believe that AI has the highest potential to deliver unique and modern solutions to the fire engineering community, noting that: 1) fire spans multiple fields (i...
Article
Full-text available
As advent by the continuous inertia toward integrating artificial intelligence into daily operations, it is a matter of time before artificial intelligence reforms the field of structural engineering. From this point of view, this paper explores how computer vision and deep learning can be applied, in combination with advanced finite element analys...
Article
This paper presents a data-driven machine learning (ML) framework for predicting failure mode and shear capacity of Ultra High Performance Concrete (UHPC) beams. To this end, a comprehensive database on 360 reported tests on UHPC beams with different geometric, fiber properties, loading and material characteristics was collected. This database was...
Article
Full-text available
The past few years have witnessed the rise of serious research efforts directed towards understanding fire-induced spalling in concrete. Despite these efforts, one continues to fall short of arriving at a thorough examination of this phenomenon and of developing a modern assessment tool capable of predicting the occurrence and intensity of spalling...
Article
Full-text available
Fiber-reinforced polymers (FRPs) are often used as externally bonded systems or internally reinforcing elements to improve the performance and resilience of concrete structures. Oftentimes, the observed response of FRP-strengthened/reinforced concrete members, whether in the field or in experiments, does not match that predicted using codal provisi...
Article
This paper presents a data-driven machine learning (ML) framework for predicting failure mode and shear capacity of Ultra High Performance Concrete (UHPC) beams. To this end, a comprehensive database on 360 reported tests on UHPC beams with different geometric, fiber properties, loading and material characteristics was collected. This database was...
Article
Full-text available
Concrete, a naturally resilient material, often undergoes a series of physio-chemical degradations once exposed to extreme environments (e.g., elevated temperatures). Under such conditions, not only concrete weakens, but also becomes vulnerable to fire-induced spalling; a complex and exceptionally random phenomenon. Despite serious efforts carried...
Article
Post-earthquake fire (PEF) damage in buildings is a hazard, which is usually not taken into consideration during structural design in buildings. Past instances of PEF events have led to severe damage and loss of lives, often higher than those experienced during the earthquake event. PEF is a complex phenomenon that requires special treatment, one t...
Article
Full-text available
The interaction between bending and shear effects in steel beams can be amplified under fire conditions due to rapid degradation in strength and stiffness properties of steel, together with temperature-induced local instability effects. This paper presents temperature-induced moment-shear (M-V) interaction phenomenon in compact (Class 1) steel beam...
Cover Page
Full-text available
I am happy to announce that the American Concrete Institute has approved a special publication (SP) to accompany our session titled, “The Concrete Industry in the Era of AI”, which will take place at the ACI convention in Baltimore, MD (March 28-April 1, 2021). I am cordially inviting you to consider submitting a short conference paper (6-8 pages)...
Article
Full-text available
Recent advancements in material sciences have led to the development of new fiber-reinforced polymer (FRP) systems that, unlike traditional FRPs, are specifically tailored to have large fracture strains that are advantageous for external strengthening applications. One such system is polyethylene terephthalate (PET) FRP, which can attain a nominal...
Article
Full-text available
The structural fire engineering community has been slowly evolving over the past few decades. While we continue to favor a classical stand toward evaluating fire resistance of structures through fire experimentations, a movement toward developing numerical assessment tools is on the rise. A close examination of notable works shows that the majority...