Exploratory Advanced 

Research Program

Physically Informed Data-Driven Methods for 

Greatly Enhancing the Use of Heterogeneous 

Supplementary Cementitious Materials in 

Transportation Infrastructure 

Research Summary Report

 
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Recommended citation: Federal Highway Administration, Physically 

Informed Data-Driven Methods for Greatly Enhancing the Use of 

Heterogeneous Supplementary Cementitious Materials in Transportation 

Infrastructure (Washington, DC: 2023) https://doi.org/10.21949/1521984
Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.

1. Report No.

FHWA-HRT-23-040

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle 

Physically Informed Data-Driven Methods for Greatly Enhancing 

the Use of Heterogeneous Supplementary Cementitious Materials 

in Transportation Infrastructure

5. Report Date 

July 2023

6. Performing Organization Code

7. Author(s) 

G. Sant (ORCID: 0000-0002-1124-5498), M. Bauchy (ORCID: 

0000-0003-4600-0631), N. Neithalath (ORCID: 0000-0002-3174-

0402), A. Kumar (ORCID: 0000-0001-7550-8034)

8. Performing Organization 

Report No.

9. Performing Organization Name and Address 

University of California, Los Angeles

Department of Civil and Environmental Engineering

420 Westwood Plaza, 5731-J Boelter Hall 

Los Angeles, CA 90095

Schatz Publishing Group

11950 W. Highland Ave.

Blackwell, OK 74631

10. Work Unit No.

11. Contract or Grant No.

693JJ3-19-50021

12. Sponsoring Agency Name and Address

Office of Corporate Research, Technology, and Innovation 

Management

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period 

Covered

Research Summary Report

September 2019–August 2022

14. Sponsoring Agency Code

HRTM-30

15. Supplementary Notes

The Contracting Officer’s representative is Jack Youtcheff (HRDI-10).

16. Abstract

This summary report shares the findings of an Exploratory Advanced Research Program project that 

investigated strategies for expanding the use of fly ash (FA) in concrete production. With an unprecedented 

massive concrete and FA dataset collection of 40,000 data records, the team conducted a series of 

experiments that used advanced material characterizations, machine-learning techniques, and numerical 

simulations to uncover the fundamental attributes governing the reactivity of FA and its suitability as an 

ordinary portland cement replacement. This work aimed to enhance the use of FA for producing more 

sustainable concrete.

17. Key Words

Fly ash, reactivity, supplementary cementitious 

materials, concrete production, machine learning, 

topological constraint theory

18. Distribution Statement

No restrictions. This document is available to the 

public through the National Technical Information 

Service, Springfield, VA 22161.

https://www.ntis.gov

19. Security Classif. (of this 

report) Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages 

10

22. Price

N/A

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

  |  I
 

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

II  |
Contents

Introduction 1

Project Overview 2

Selecting and Characterizing Physical/Chemical Indicators for the 

Reactivity of FA 2

Develop Experimental and Synthetic Datasets to Train ML Models 

to Predict the Engineering Properties of [PC + FA] Mixtures 3

Develop a Set of ML Models to Optimize and 

Predict the Performance of [PC + FA] Mixtures 6

Predicting Concrete’s Strength by ML Balance

Between Accuracy and Complexity of Algorithms 6

Using ML to Predict Concrete’s Strength: Learning from Small Datasets 7

Interpreting the Strength Activity Index (SAI) of FA with ML 8

Conclusions 9

References 10

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

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LIST OF FIGURES

Figure 1. Schematic. Proposed ML-based screening approach. 3

Figure 2. Graph. Heat-flow rate profiles of randomly selected [PC+ FA] binders. 4

Figure 3. Graph. The standalone DF model predictions of heat-flow rate. 5

Figure 4. Graph. The predictions of compressive strength of [PC+ FA] binders 

as produced by the DF model against the testing dataset. 6

Figure 5. Graph. Permutation importance of each of the features considered. 7

LIST OF TABLES

Table 1. Statistical parameters pertaining to the prediction performance of DF and 

[DF + Segmentation] models on 72-h heat-flow rate. 5

Table 2. The statistical parameters pertaining to the prediction performance of the 

DF and analytical models on [PC+ FA] binders’ compressive strength. 6

LIST OF ABBREVIATIONS

AEA air-entraining admixture 

ANN artificial neural network 

Ca calcium 

CAS calcium aluminosilicate 

CO2

 carbon dioxide 

DF deep forest 

EAR exploratory advanced research 

FA fly ash 

MAE mean absolute error 

MAPE mean absolute percentage error 

ML machine learning 

PC portland cement

R Pearson correlation coefficient 

R2

 coefficient of determination 

RMSE root mean squared error 

SCM supplementary cementitious materials

TCT topological constraint theory 

WRA water-reducing admixture 

XRF x-ray fluorescence

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

IV  |
Concrete is the dominant material used in the 

construction of buildings and infrastructure.(1)

However, the production of ordinary portland 

cement (PC) is associated with substantial carbon 

dioxide (CO2) emissions, estimated at nearly 

9 percent of the global CO2 emissions.(2) As a result, 

the industry has sought to replace PC in concrete 

with supplementary cementitious materials (SCMs). 

Fly ash (FA), a residue from coal combustion, is 

currently the only SCM available in sufficient 

abundance to replace PC in concrete.(3) However, 

FA’s diverse chemical composition and the presence 

of glassy and crystalline 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 FA can greatly affect the performance of the 

concrete with which it is mixed. Even similar FAs can 

result in vastly different concrete behavior. As a 

result, over the past decades, FA has only been used 

to replace a limited amount of PC in concrete (e.g., 

≤25 percent by mass) because it has had limited 

success as a high-volume replacement.(3)

Introduction

In response to this issue, a research team conducted 

a study called “Physically Informed Data-Driven 

Methods for Greatly Enhancing the Use of 

Heterogeneous Supplementary Cementitious 

Materials in Transportation Infrastructure.” With an 

unprecedented massive concrete and FA dataset 

collection of 40,000 data records, the 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 FA and its 

suitability as a PC replacement. This work aimed 

to enhance the use of FA for producing more 

sustainable concrete.

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

  |  1
The researchers broke down the project into a series of tasks that applied ML techniques to 

create analytical models that could predict the molecular characteristics and reactivity of FA, 

 FA’s interaction with PC, and the subsequent engineering properties of concrete containing FA.

SELECTING AND CHARACTERIZING PHYSICAL/

CHEMICAL INDICATORS FOR THE REACTIVITY 

OF FA 

The reactivity of FA’s amorphous phase determines 

the ability of an FA to replace cement in concrete. But 

characterizing FA’s amorphous phase is complex and 

cost prohibitive—which has thus far prevented any 

high-throughput screening of FAs to assess their 

suitability as SCMs. To better understand FA and its 

reactivity, the research team first developed an 

analytical model to predict the atomic topology of 

calcium (Ca) aluminosilicate (CAS) glasses. CAS is 

the essential component of the amorphous phase of 

both Class C (Ca-rich) and Class F (Ca-poor) FAs. 

This model was established based on topological 

constraint theory (TCT), which predicts various 

properties of oxide glasses as a function of their 

composition and structure.

TCT has been a key enabler in developing predictive 

models that relate the composition and structure of 

glasses to their properties.(4,5)

 TCT simplifies complex 

disordered atomic networks into simpler nodes (the 

atoms) connected to each other by chemical bonds 

(their topological constraints). In structural glasses, 

topological constraints comprise the radial two-body 

bond stretching and angular three-body bond-bending 

constraints. The number of constraints per atom (nc) 

then offers a simple, reduced-dimensionality metric 

that is often correlated with macroscopic properties.(6)

To test this model, the researchers engaged in 

classical molecular dynamics simulations of 231 CAS 

glasses using the Large-Scale Atomic/Molecular 

Massively Parallel Simulator package.(4,5,6) CAS 

glass samples were created using the conventional 

melt-quench method (a process of melting and cooling

the material). The glasses’ molecular structure was 

then analyzed, and the atoms within each structure 

were labeled. The interaction of the various atoms 

within the molecular structure was also examined, 

especially areas that were Ca rich and aluminum rich. 

The subsequent model predicted the average topology 

of CAS glasses based on their composition. The model 

was used to determine the state of rigidity of CAS 

glasses based on their temperature and composition. 

This model yields the state of rigidity (flexible, isostatic, 

or stressed rigid) of CAS systems as a function of 

composition and temperature. These results reveal the 

existence of correlations between network topology 

and glass-forming ability—that is, the propensity for a 

liquid to form a disordered glass or an ordered crystal 

upon quenching. This experiment suggested that 

glass-forming ability is encoded in the network 

topology of the liquid state (i.e., during the quenching 

of the FAs from the liquid state) rather than that of the 

glassy state. This finding is important since both the 

state of rigidity of the atomic network of fly ashes and 

the fraction of the glassy phase therein govern their 

reactivity (i.e., their ability to dissolve and react in 

aqueous environments to contribute to the strength 

development in concrete).

Based on this understanding of the network typology 

of FA’s glassy amorphous phase, the team developed 

a process for screening the reactivity of FAs based on 

fast, inexpensive bulk characterization called X-ray 

fluorescence (XRF). The researchers trained an 

artificial neural network (ANN) model (using a dataset 

of more than 100 FAs) that mapped out the bulk XRF 

composition of an FA to accurately predict the mass 

Project Overview

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

2  |
fraction of FA’s amorphous phase and network 

topology. This new method could help maximize 

the beneficial use of FAs obtained from routine 

production as well as identify opportunities for the 

reclamation of ashes that are presently stored in 

impoundments. As figure 1 shows, easily measurable

FA properties are mapped to key performance metrics 

to inform decisionmaking regarding which end usage 

should be preferred.

DEVELOP EXPERIMENTAL AND SYNTHETIC DATASETS 

TO TRAIN ML MODELS TO PREDICT THE ENGINEERING 

PROPERTIES OF [PC + FA] MIXTURES

The researchers developed a deep forest (DF) model 

to predict time- and composition-dependent hydration 

kinetics and compressive strength in relation to the 

mixture design of [PC + FA] binders and the physical 

and chemical properties of 10 FAs. The DF model 

was coupled with the segmentation technique 

(inserting different types of samples into specific 

segments during the training of the model) to enhance 

the prediction performance. Finally, through inference 

of the intermediate and final outputs of the DF model, 

a simple, closed-form analytical model was developed 

to predict compressive strength and reveal the 

correlations between mixture design and compressive 

strength of [PC + FA] binders.

The researchers analyzed the chemical composition 

of obtained PC and FA, produced [PC + FA] concrete 

samples, and measured each sample’s heat flow 

profiles of the [PC+FA] concrete binders. Heat-flow is 

the amount of heat following the hydration of PC that 

causes a temperature rise in cement. The researchers 

also measured the compressive strength of each 

sample. A random portion of the measured heat-flow 

profiles and compressive strength datapoints from 

the [PC + FA] concrete binders were input into a DF 

model. Five statistical parameters were employed to 

assess the performance of DF models on the testing 

datasets: mean absolute percentage error (MAPE), 

mean absolute error (MAE), Pearson correlation 

coefficient (R), root mean squared error (RMSE), 

and coefficient of determination (R2

).

© 2021 American Chemical Society. 

Figure 1. Schematic. Proposed ML-based screening approach. 

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

  |  3
Project Overview

© 2022 Missouri University of Science and Technology.

Figure 2. Graph. The standalone DF model predictions of heat-flow rate.

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

4  |

A. [PC+ FA] binder PC-1 + 20 percent FA-C B. [PC+ FA] binder PC-1 + 50 percent FA-G 

compared to experimental measurements compared to experimental measurements 

in the testing database. in the testing database. 

C. [PC+ FA] binder PC-2 + 30 percent FA-B D. [PC+ FA] binder PC-2 + 40 percent FA-F 

compared to experimental measurements compared to experimental measurements 

in the testing database. in the testing database. 
ML Model R

(Unitless)

R2

(Unitless)

MAE

(mW gcem-1)

MAPE 

(Percent)

RMSE 

(mW gcem-1)

DF 0.9476 0.8981 0.1471 13.27 0.2463

DF + 

Segmentation 0.9743 0.9871 0.0701 7.43 0.1197

Table 1. Statistical parameters pertaining to the prediction performance of DF and [DF + Segmentation] 

models on 72-h heat-flow rate. 

The DF model was used to predict heat-flow rate 

profiles of 10 randomly selected [PC + FA] binders 

every hour for a 72-h hydration period. Figure 2 shows 

predicted heat-flow rate profiles of representative 

[PC + FA] binders as produced by the standalone 

DF model against experimental measurements. To 

visually compare the predicted and measured values, 

the entire heat-flow rate spectrums are included. The 

statistical parameters pertaining to the prediction 

performance of the 72-h hydration period on the 

testing dataset are itemized in table 1.

Due to the small-volume dataset (containing only 101 

binders), the DF model was subsequently fine-tuned 

to reliably predict the heat-flow rate of FA. The team 

integrated a hydration theory-guided segmentation 

technique into the model to reduce the complexity of the 

database and enhance the model’s prediction accuracy. 

The segmentation technique is based on the hypothesis 

that, in the same segment, the hydration behavior of 

all [PC + FA] binders should be driven by the same 

mechanisms and, therefore, should manifest as similar 

kinetic (heat flow) profiles. The heat-flow rate was 

divided into three segments:

1. The first segment: The initial and induction 

periods (the first two stages of the hydration 

reaction—the chemical reaction in the concrete 

production process when water is added to 

cement). In this segment, high heat release is 

observed in the first hour, and the heat-flow 

rate for the remaining duration is low. 

2. The second segment: The acceleration period 

(the third stage of the hydration reaction, when 

it is most intense). 

3. The third segment: The deceleration period 

(the final stage of the hydration reaction, 

where the heat-flow rate diminishes). 

The research team developed an algorithm to find 

the optimal thresholds for the segmentation. Figure 3

shows the thresholds for the different segments in 

representative binders.

After the [DF + Segmentation] model received the 

training dataset, the segmentation algorithm separated 

© 2022 Missouri University of Science and Technology.

Note: Based on their kinetic behaviors, the profiles are divided into 

three segments: initial and induction periods, acceleration period, 

and deceleration period.

Figure 3. Graph. Heat-flow rate profiles of randomly 

selected [PC+ FA] binders.

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

  |  5
Project Overview

ML Model R

(Unitless)

R2

(Unitless)

MAE

(MPa)

MAPE 

(Percent)

RMSE 

(MPa)

DF 0.9368 0.8775 3.939 11.34 5.221

Analytical 

model 0.9031 0.8156 4.051 11.6 5.322

Table 2. The statistical parameters pertaining to the prediction performance of the DF and analytical 

models on [PC + FA] binders’ compressive strength. 

the training dataset into three sub-datasets. Then, three 

parallel DF models were independently trained with 

three sub-datasets to find the input-output correlations 

for each segment. When a testing dataset was 

implemented into the model, three parallel DF models 

predicted the heat-flow rate of [PC + FA] binders with 

respect to their segments. Later, the segmentation 

algorithm combined the outcomes from DF models 

to produce the entire heat-flow rate profile. Table 1

illustrates the improved predictive power of the DF 

model integrated with the segmentation algorithm.

For predicting binder compressive strength, the team 

first tested the compressive strength of binder cube 

specimens, [PC-1 + FA] and [PC-2 + FA]. These 

measurements were compared to predictions based 

on the DF model that was trained with a database of 

compressive strength from 92 unique [PC + FA] 

binders (i.e., 2 plain PCs and 90 PCs replaced by 

FAs). As shown in figure 4 and table 2, predictions of 

compressive strength against the testing dataset are 

reliable, with R2

 and RMSE being 5.22 MPa. The 

typical measurement error of compressive strength 

is 5 MPa, where the prediction error is close to the 

experimental error.(7)

DEVELOP A SET OF ML MODELS TO OPTIMIZE 

AND PREDICT THE PERFORMANCE OF 

[PC + FA] MIXTURES 

Predicting Concrete’s Strength by ML Balance 

Between Accuracy and Complexity of Algorithms

The team leveraged a dataset (comprising 10,264 

observations) of measured compressive strength 

values obtained from actual job-site concrete mixtures 

and their corresponding mixture proportions.(8) The 

reported mixture proportions reflected the actual 

mixture proportions, i.e., based on the batch weights 

of industrially produced concretes that were either 

truck- or central-plant mixed. Furthermore, all the 

strength measurements reported in the datasets used 

ASTM C150-compliant Type Ι/ΙΙ PC. Class F FA 

compliant with ASTM C618 was used in select cases.(9,10)

The team conducted a permutation importance 

analysis to select the concrete characteristics in the 

© 2022 Missouri University of Science and Technology.

Note: The dashed line is the ideal prediction. The solid lines are ±10 percent 

error bounds. 

Figure 4. Graph. The predictions of compressive strength 

of [PC + FA] binders as produced by the DF model against 

the testing dataset. 

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

6  |
datasets to be used as inputs for the ML models 

considered.(11) This analysis (conducted based on the 

ANN model) randomly shuffled each characteristic and 

tracked the associated loss in accuracy to determine 

which characteristics were most important. As shown

in figure 5, the six most influential characteristics 

controlling concrete’s strength were (in order of 

decreasing importance):

1. Water-to-cement ratio (w/c, mass basis).

2. Fine aggregate mass fraction.

3. Water-reducing admixture (WRA) dosage.

4. Coarse aggregate mass fraction.

5. FA mass fraction.

6. Air-entraining admixture (AEA) dosage. 

Once the important characteristics were identified, 

the researchers employed multiple ML methods:

• Polynomial regression.

• ANN.

• Random forest.

• Boosted tree.

The researchers then tested the accuracy of each 

of these ML models, finding that they all predicted 

the compressive strength of [PC + FA] concrete well. 

Random forest, in particular, provided the most 

optimal balance between accuracy, complexity, 

and interpretability.

Using ML to Predict Concrete’s Strength: 

Learning from Small Datasets

The researchers also conducted a study using 

ML techniques to predict [PC + FA] concrete’s 

compressive strength with a smaller dataset. 

The dataset used in this study comprises the 28-d 

compressive strength of 10,264 commercial concretes 

and their associated mixture proportions.(8) All the 

mixtures were cast using ASTM C150-compliant 

Type Ι/ΙΙ cement and Class F FA compliant with 

ASTM C618, where FA is a by-product of coal power 

plants that can be used as SCM to replace cement 

in concrete.(12,13,14)

 The seven most influential features 

were considered in this study:

1. W/c ratio (in this case, the ratio between the 

mass of water and that of cement and FA).

2. Cement fraction.

3. FA fraction.

4. Fine aggregate fraction.

5. AEA dosage (used for enhancing 

concrete durability).

6. WRA dosage (used for increasing 

concrete early-stage workability).

For normalization purposes, features 2–4 were

considered solid-weight fractions. The fraction 

of coarse aggregates was excluded as it was 

the same as features 2–4.

© 2022 University of California, Los Angeles.

Figure 5. Graph. Permutation importance of each of 

the features considered.

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

  |  7
Project Overview

The team used 70 percent of the strength 

observations from its dataset as a model training for 

three ML algorithms (polynomial regression, ANN, and

random forest). They then evaluated the accuracy of 

each model based on its RMSE and coefficient of 

determination. Each model’s learning efficiency—how 

each model learns how to predict concrete strength 

as it is exposed to increasing numbers of training 

examples—was also evaluated. Since the dataset 

for this study was relatively small, the researchers 

assessed the ability to make generalizations based 

on each model.

Interpreting the Strength Activity Index (SAI) 

of FA with ML

To promote the use of high-volume FA in concrete, the 

researchers used ML methods to infer the SAI of FAs. 

SAI is an indicator of the quality of additional 

materials mixed into cement when producing 

concrete. Leveraging a dataset comprising 

2,158 FA samples, the researchers trained 

ANN models to predict 28-d SAI based on 

the sole knowledge of ASTM C618 material 

attributes.(13) The ANN model could accurately 

predict the 28-d SAI, where the prediction 

error averaged on the testing samples is 

merely 2.2 percent. The results demonstrated

that SAI is a complex property that does not 

systematically follow the conventional Class 

C/F classification. To gain a deeper insight 

into this matter, the team further quantified 

the influence of each attribute on SAI as 

captured by the ML model.

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

8  |
Conclusions

Overall, the results of this study demonstrated 

 the ability of ML techniques to help better 

understand FA and predict its interaction 

with cement to create concrete and the concrete 

mixture’s subsequent engineering properties.

The researchers found that by examining CAS 

glass and its molecular structure as a proxy, they 

could develop a model for FA that could predict 

its reactivity potential. This work can help facilitate 

the mass use of FA to create more sustainable 

concrete by enabling screening using XRF. Bulk 

screening can help decipher which FA would be 

most suitable for concrete production.

In addition, from the work of this research team, 

the interaction of FA and cement, the [PC + FA] 

binders, and their compressive strength could be 

predicted using the DF model. The researchers 

demonstrated that this result could be accomplished 

with a limited dataset.

The results from the SAI test were the first time 

that an ML model was successfully used for 

predicting SAI. In addition, by implementing a 

model interpretation technique, the researchers 

further decoded the black-box ANN model. The 

team also found how the individual material 

attributes synergistically determine SAI. From 

a practical perspective, the accurate prediction 

of SAI can significantly promote the optimal use 

of FA for sustainable concrete construction.

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

  |  9
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Engineering Handbook. Boca Raton, FL: CRC Press. 

11. Young, B. A., A. Hall, L. Pilon, P. Gupta, and G. 

Sant. 2019. “Can the Compressive Strength of 

Concrete Be Estimated From Knowledge of the 

Mixture Proportions?: New Insights From Statistical 

Analysis and Machine Learning Methods.” Cement 

and Concrete Research 115, 379–388.

12. ASTM International. 2018. Standard Specification 

for Portland Cement. ASTM C150/C150M-17. 

West Conshohocken, PA: ASTM International.

13. 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.

14. Conn, R. E., K. Sellakumar, and A. E. Bland. 1999. 

Utilization of CFB Fly Ash for Construction 

Applications. Presented at the 15th International 

Conference on Fluidized Bed Combustion. 

Savannah, GA: U.S. Department of Energy Office 

of Scientific and Technical Information.

REFERENCES

EAR PROGRAM—PHYSICALLY INFORMED DATA-DRIVEN METHODS FOR GREATLY ENHANCING THE USE OF HETEROGENEOUS 

SUPPLEMENTARY CEMENTITIOUS MATERIALS IN TRANSPORTATION INFRASTRUCTURE—RESEARCH SUMMARY REPORT

10  |

Getting Involved with the EAR Program

To take advantage of a broad variety of scientific and engineering discoveries, the EAR 

Program involves both traditional stakeholders (State department of transportation 

researchers, University Transportation Center researchers, and Transportation Research 

Board committee and panel members) and nontraditional stakeholders (investigators from 

private industry, related disciplines in academia, and research programs in other countries) 

throughout the research process.

EXPLORATORY ADVANCED RESEARCH

FHWA-HRT-23-040

HRTM-30/07-23(WEB)E

Recommended citation: Federal Highway Administration, 

Physically Informed Data-Driven Methods for Greatly 

Enhancing the Use of Heterogeneous Supplementary 

Cementitious Materials in Transportation Infrastructure

 (Washington, DC: 2023) https://doi.org/10.21949/1521984