EAR PROGRAM—RESEARCH RESULTS | 1

EAR Program—Research Results

Physically Informed Data-Driven Methods for Greatly Enhancing the Use of Heterogeneous 

Supplementary Cementitious Materials in Transportation Infrastructure

INSTITUTION: University of California, Los Angeles

COMPLETED: 2022

OBJECTIVE: To enhance the use of fly ash for producing more sustainable concrete.

RESULTS: Concrete is the dominant material used in the 

construction of buildings and infrastructure.(1)

 However, the 

production of ordinary portland cement (OPC) is associated 

with substantial carbon dioxide (CO2

) emissions, estimated 

at nearly 9 percent of the global carbon emissions.(2)

 To 

counteract the CO2

 impact of OPC production and use, the 

industry has sought to replace OPC in concrete with 

supplementary cementitious materials (SCMs). Fly ash, a 

residue from the combustion of coal, is currently the only 

SCM available in sufficient abundance to replace OPC in 

concrete.(3)

 However, fly ash’s diverse chemical composition 

and the presence of crystalline (i.e., ordered) and 

noncrystalline (i.e., disordered) phases can make it difficult 

to use in concrete production. Although it is defined as 

either Class C or F, the specific composition of a certain fly 

ash can greatly affect the performance of the concrete with 

which it is mixed. Even similar fly ashes can result in vastly 

different concrete behavior. As a result, over the past 

decades, fly ash has had limited success as a high-volume 

substitute, replacing a limited amount of OPC in concrete 

(e.g., ≤25 percent by mass).(3)

With an unprecedented massive concrete and fly ash 

dataset collection of 40,000 data records, a research team 

conducted a series of experiments that used advanced 

material characterizations, machine-learning (ML) 

techniques, and numerical simulations to uncover the 

fundamental attributes governing the reactivity of fly ash 

and its suitability as an OPC replacement. This work aimed 

to enhance the use of fly ash for producing more 

sustainable concrete.

Using topological constraint theory and classical molecular 

dynamics simulations, the researchers developed an 

analytical model to predict the atomic topology of calcium 

aluminosilicate (CAS) glasses that make up the essential 

components of fly ash’s noncrystalline (i.e., glassy) phase, 

that is, the most reactive phase of fly ashes (which 

contribute to increasing concrete’s strength). This model 

could be used to accurately estimate the state of rigidity 

(flexible, isostatic, or stressed-rigid) of CAS glasses based 

on their chemical composition and temperature. The results 

revealed that the glass-forming ability of CAS glasses 

(which is closely related to the reactivity of fly ash) is 

encoded in its network topology (molecular structure), which 

is captured by the number of constraints per atom (nc

). 

Importantly, this finding indicates that the nc

 of the 

amorphous phase of a fly ash can serve as a reliable 

structural proxy for its reactivity. The researchers then used 

an ML-based methodology to predict fly ash’s rigidity (i.e., 

nc

) based on the sole knowledge of its bulk chemical 

composition. This approach can enable the rapid screening 

of reactive fly ashes via fast, inexpensive bulk 

characterization techniques (e.g., x-ray fluorescence). The 

team’s predictive model can be used to maximize the 

beneficial use of fly ashes obtained from routine power 

plant production and enhance the reclamation of reactive fly 

ashes that are presently stored in impoundments.

The researchers developed a generic theoretical framework 

that can produce reliable predictions of binder properties 

that bring together portland cement and fly ash [PC + FA] to 

create concrete. This framework relied on a deep forest 

(DF) model, which consists of an ensemble of decision 

trees. Each tree offers a distinct prediction, which depends 

on whether or not the condition attached to each of its 

branches are satisfied. The DF model is trained with a 

collection of experimental data on the strength development 

of binders containing various fractions of cement being 

replaced by fly ash. Furthermore, theoretical knowledge of 

fly ash’s composition-structure correlations and cement’s 

hydration mechanism (i.e., which is governed dimensionally 

by the number of constraints per atom nc

 previously 

mentioned) are infused into the DF model to boost its 

prediction performance. As a result, the DF model can 

accurately predict composition- and time-dependent 

hydration kinetics (i.e., the rate at which the reactive solids 

dissolve and react to form a cement paste) and compressive 

strength (the capacity of concrete to withstand loads before 

failure) of [PC + FA] binders. More importantly, the study 

derived a simple, closed-form analytical model that can 

robustly map the compositional attributions of cement and 

fly ash to the compressive strength of the binders. 
2 | EAR PROGRAM—RESEARCH RESULTS

EAR Program—Research Results 

This analytical model can offer important insights into the 

interactive effects of cement and fly ash on the strength 

development of [PC + FA] binders.

Due to the complex chemical reactions involved with 

cement hydration (the reaction between water and cement 

during the mixing process) and the interactions between 

different phases in concrete, reliable predictions of the 

strength of fly-ash-containing concrete materials (and many 

other key properties) remain unavailable through 

conventional approaches. As an alternative, ML offers a 

new pathway to developing powerful predictive models for 

concrete materials. After curating a collection of large-scale 

concrete datasets based on raw data obtained from actual 

concrete production, the research team demonstrated the 

potential of using different ML models to accurately predict 

the strength development of concrete. 

The researchers first compared the performance of several 

prevailing ML algorithms in predicting the 28-d compressive 

strength (the strength of concrete 28 d after being cast) 

based on the knowledge of the mixture proportion, where 

the tradeoff between model accuracy, simplicity, and 

interpretability across those ML algorithms was specifically 

studied. The research team also investigated the influence 

of the training dataset’s size on the accuracy of the team’s 

ML models—as high-quality concrete strength data is 

limited. In that regard, several techniques were studied that 

could be used to improve the efficiency of ML methods 

when applied to small concrete datasets. 

To enable the use of high-volume fly ash in concrete, the 

researchers also looked into using ML to infer the strength 

activity index (SAI) (an indicator of the quality of additional 

materials mixed into cement) of fly ashes based on the 

knowledge of their key material attributes. Based on a large 

fly ash dataset curated from the team’s testing records, the 

researchers trained an ML model that offered accurate 

predictions of the 28-d SAI based on the sole knowledge of 

the fly ash’s ASTM C618 attributes.(4)

 These models allowed 

the researchers to clarify how each of the chemical and 

physical attributes of fly ash affects their ability to replace 

cement in concrete.

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4. ASTM International. 2010. Standard Specification for Coal 

Fly Ash and Raw or Calcined Natural Pozzolan for Use in 

Concrete. ASTM C618-08a. West Conshohocken, PA: 

ASTM International. 

© 2021 American Chemical Society.

Figure 1. Schematic. Proposed ML-based screening approach.
EAR PROGRAM—RESEARCH RESULTS | 3

RESOURCES:

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EAR Program—Research Results 

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Publication No.: FHWA-HRT-23-048

HRTM-30/10-23(WEB)E 

https://doi.org/10.21949/1521995