Understanding Complex Chemo-Physical Distress and Mitigation Mechanisms of
Structural Concrete Using a Physics-Informed Machine Learning Method
Authors: Rahman Md Asif 
Published: January 2025

Concrete is a widely utilized construction material globally, composed of a
mixture of cement, water, and aggregates. It possesses exceptional strength and
durability, making it an ideal choice for various construction projects.
Nonetheless, concrete structures undergo degradation over time, necessitating a
deeper understanding of the underlying chemical and physical processes. One
crucial chemical process in concrete formation is cement hydration, which
involves intricate reactions and physical transformations, resulting in a solid
and enduring material. This exothermic process yields calcium hydroxide (CH) and
releases heat, contributing to temperature fluctuations that can impact the
early-age performance of concrete. It also plays a role in energy production and
carbon emissions. To address these challenges, the use of supplementary
materials is a prudent strategy. Selecting appropriate supplementary
cementitious materials (SCMs) can reduce carbon emissions and enhance the
overall strength of concrete. On the other hand, a sulfate attack arises from
harmful chemical reactions that lead to the formation of ettringite and
undesirable expansion within concrete. This issue can occur when pyrrhotite-rich
aggregates react with the hydrated products of the cement paste, resulting in a
series of chemical reactions that impact the long-term performance of concrete.
To tackle these challenges, there is a pressing need for a robust model that can
predict the chemo-physical behavior of cement-concrete. This research delves
into the application of physics-informed machine learning (PIML), an
interdisciplinary approach that combines physics and machine learning (ML) to
improve model performance and interpretability. The primary emphasis of this
study lies in the creation of physics-informed neural network (PINN) models
aimed at quantifying and mitigating distress issues in cement-concrete. The
outcomes of this research are documented in three individual chapters, each
corresponding to a separate journal manuscript.

In the first manuscript, the focus lies on the introduction of PINN-CHK, a
physics-informed neural network model designed to predict cement hydration
kinetics. Its primary objective is to explore temperature variations during the
initial phases of cement paste hydration. PINN-CHK harnesses data-driven
methodologies to construct an accurate predictive model, incorporating material
characteristics and maturity functions associated with cement hydration. It
adeptly aligns with both empirical findings and the fundamental principles of
physics, resulting in a simulation that operates without the need for
traditional mesh structures.

The second manuscript is dedicated to the development of EcoBlendNet, an
innovative physics-informed neural network, designed to scrutinize carbon
emissions during the enhancement of cement hydration with supplementary
cementitious materials. EcoBlendNet amalgamates empirical data with the chemical
and physical facets of cement hydration in a heated cement paste, providing
precise predictions of concrete maturity and compressive strength. It
accomplishes this by accurately capturing the temperature increases during the
early stages of hydration for various mixtures, including Portland cement,
cement-fly ash blends, and cement-slag blends. Notably, SCMs prove effective in
mitigating temperature rises while maintaining strength development.

The third manuscript addresses PyrrhotiteNet, a pioneering sequential structure
based on a physics-informed neural network. PyrrhotiteNet aims to handle coupled
differential equations conjugating both pyrrhotite oxidation and sulfate attack
in concrete. By leveraging experimental data and incorporating physical
insights, PyrrhotiteNet allows the model to quantify sulfate ion concentration
and concrete expansion, facilitating the assessment and prediction of sulfate
attack's extent, distribution, and progression. The validated PyrrhotiteNet
aligns with real dam measurements and can predict future structural displacement
with minimal training data. In essence, the developed PyrrhotiteNet represents a
mesh-free, data-driven platform for accurate predictions of concrete expansion
and displacement, rivaling traditional finite element solvers.

Hence, the ultimate result of this study reveals a groundbreaking
physics-informed modeling approach that effectively bridges the gap between
theoretical principles and real-world applications. This approach offers an
enticing departure from traditional finite element solvers and machine learning
techniques, fostering a deeper comprehension of cement hydration, use of SCMs,
and sulfate attack in concrete, to precisely predict maturity, strength
development, and overall concrete performance. When integrated into
infrastructure asset management methodologies, it carries the potential to
enhance the design of durable concrete structures, facilitating prompt
maintenance as well as eco-friendly solution.