Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes

Lubna K. Najjar; Haitham Elnakar

doi:10.1007/978-3-032-16389-9_23

Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes

Lubna K. Najjar
, Haitham Elnakar^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study evaluates the environmental performance of optimized and non- optimized concrete mixes. The optimized mix was reconstructed using constraints and objective functions of a published machine learning optimization model. A cradle-to- gate life cycle assessment, conducted using the ReCiPe Endpoint method (H), was employed to quantify trade-offs between concrete performance and environmental impacts. Results show that while the optimized mix achieves a comparable compressive strength of about 50 MPa, it exhibits a higher binder intensity (8.50 vs. 7.85 kg/m³·MPa). Despite this, it achieves a 10% reduction in embodied energy due to increased use of supplementary cementitious materials. The optimized mix also demonstrates lower impacts in global warming and resource scarcity categories. However, it incurs higher burdens in several other areas, including toxicity, ecotoxicity, and water consumption. These findings highlight the trade-offs inherent in performance-driven optimization and emphasize the need to integrate environmental impact metrics into concrete mix design to support genuinely sustainable construction practices.

Original language	English
Title of host publication	International Conference on Sustainability and Innovation Processes and Systems - SUSTAIN Bahrain 2025
Editors	Giuseppe Cantafio, Layla Alali, Shahid Maqsood, Alaaeddine Ramadan, Mansoor Farooq, Mohammad Al-Shoqran, Hanan Naser
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	256-265
Number of pages	10
ISBN (Print)	9783032163882
DOIs	https://doi.org/10.1007/978-3-032-16389-9_23
State	Published - 2026
Event	International Conference on Sustainability and Innovation Processes and Systems, ICSIPS 2025 - Riffa, Bahamas Duration: 23 Jun 2025 → 24 Jun 2025

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1806 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Sustainability and Innovation Processes and Systems, ICSIPS 2025
Country/Territory	Bahamas
City	Riffa
Period	23/06/25 → 24/06/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 6 Clean Water and Sanitation
SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action

Keywords

Embodied Energy
Life Cycle Assessment
Machine Learning
Optimization
Sustainable Construction

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-032-16389-9_23

Cite this

Najjar, L. K., & Elnakar, H. (2026). Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes. In G. Cantafio, L. Alali, S. Maqsood, A. Ramadan, M. Farooq, M. Al-Shoqran, & H. Naser (Eds.), International Conference on Sustainability and Innovation Processes and Systems - SUSTAIN Bahrain 2025 (pp. 256-265). (Lecture Notes in Networks and Systems; Vol. 1806 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-16389-9_23

Najjar, Lubna K. ; Elnakar, Haitham. / Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes. International Conference on Sustainability and Innovation Processes and Systems - SUSTAIN Bahrain 2025. editor / Giuseppe Cantafio ; Layla Alali ; Shahid Maqsood ; Alaaeddine Ramadan ; Mansoor Farooq ; Mohammad Al-Shoqran ; Hanan Naser. Springer Science and Business Media Deutschland GmbH, 2026. pp. 256-265 (Lecture Notes in Networks and Systems).

@inproceedings{19e5cf8142a441d5978613e6205b8dbb,

title = "Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes",

abstract = "This study evaluates the environmental performance of optimized and non- optimized concrete mixes. The optimized mix was reconstructed using constraints and objective functions of a published machine learning optimization model. A cradle-to- gate life cycle assessment, conducted using the ReCiPe Endpoint method (H), was employed to quantify trade-offs between concrete performance and environmental impacts. Results show that while the optimized mix achieves a comparable compressive strength of about 50 MPa, it exhibits a higher binder intensity (8.50 vs. 7.85 kg/m3·MPa). Despite this, it achieves a 10\% reduction in embodied energy due to increased use of supplementary cementitious materials. The optimized mix also demonstrates lower impacts in global warming and resource scarcity categories. However, it incurs higher burdens in several other areas, including toxicity, ecotoxicity, and water consumption. These findings highlight the trade-offs inherent in performance-driven optimization and emphasize the need to integrate environmental impact metrics into concrete mix design to support genuinely sustainable construction practices.",

keywords = "Embodied Energy, Life Cycle Assessment, Machine Learning, Optimization, Sustainable Construction",

author = "Najjar, \{Lubna K.\} and Haitham Elnakar",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; International Conference on Sustainability and Innovation Processes and Systems, ICSIPS 2025 ; Conference date: 23-06-2025 Through 24-06-2025",

year = "2026",

doi = "10.1007/978-3-032-16389-9\_23",

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editor = "Giuseppe Cantafio and Layla Alali and Shahid Maqsood and Alaaeddine Ramadan and Mansoor Farooq and Mohammad Al-Shoqran and Hanan Naser",

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Najjar, LK & Elnakar, H 2026, Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes. in G Cantafio, L Alali, S Maqsood, A Ramadan, M Farooq, M Al-Shoqran & H Naser (eds), International Conference on Sustainability and Innovation Processes and Systems - SUSTAIN Bahrain 2025. Lecture Notes in Networks and Systems, vol. 1806 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 256-265, International Conference on Sustainability and Innovation Processes and Systems, ICSIPS 2025, Riffa, Bahamas, 23/06/25. https://doi.org/10.1007/978-3-032-16389-9_23

Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes. / Najjar, Lubna K.; Elnakar, Haitham.
International Conference on Sustainability and Innovation Processes and Systems - SUSTAIN Bahrain 2025. ed. / Giuseppe Cantafio; Layla Alali; Shahid Maqsood; Alaaeddine Ramadan; Mansoor Farooq; Mohammad Al-Shoqran; Hanan Naser. Springer Science and Business Media Deutschland GmbH, 2026. p. 256-265 (Lecture Notes in Networks and Systems; Vol. 1806 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes

AU - Najjar, Lubna K.

AU - Elnakar, Haitham

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - This study evaluates the environmental performance of optimized and non- optimized concrete mixes. The optimized mix was reconstructed using constraints and objective functions of a published machine learning optimization model. A cradle-to- gate life cycle assessment, conducted using the ReCiPe Endpoint method (H), was employed to quantify trade-offs between concrete performance and environmental impacts. Results show that while the optimized mix achieves a comparable compressive strength of about 50 MPa, it exhibits a higher binder intensity (8.50 vs. 7.85 kg/m3·MPa). Despite this, it achieves a 10% reduction in embodied energy due to increased use of supplementary cementitious materials. The optimized mix also demonstrates lower impacts in global warming and resource scarcity categories. However, it incurs higher burdens in several other areas, including toxicity, ecotoxicity, and water consumption. These findings highlight the trade-offs inherent in performance-driven optimization and emphasize the need to integrate environmental impact metrics into concrete mix design to support genuinely sustainable construction practices.

AB - This study evaluates the environmental performance of optimized and non- optimized concrete mixes. The optimized mix was reconstructed using constraints and objective functions of a published machine learning optimization model. A cradle-to- gate life cycle assessment, conducted using the ReCiPe Endpoint method (H), was employed to quantify trade-offs between concrete performance and environmental impacts. Results show that while the optimized mix achieves a comparable compressive strength of about 50 MPa, it exhibits a higher binder intensity (8.50 vs. 7.85 kg/m3·MPa). Despite this, it achieves a 10% reduction in embodied energy due to increased use of supplementary cementitious materials. The optimized mix also demonstrates lower impacts in global warming and resource scarcity categories. However, it incurs higher burdens in several other areas, including toxicity, ecotoxicity, and water consumption. These findings highlight the trade-offs inherent in performance-driven optimization and emphasize the need to integrate environmental impact metrics into concrete mix design to support genuinely sustainable construction practices.

KW - Embodied Energy

KW - Life Cycle Assessment

KW - Machine Learning

KW - Optimization

KW - Sustainable Construction

UR - https://www.scopus.com/pages/publications/105030652023

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DO - 10.1007/978-3-032-16389-9_23

M3 - Conference contribution

AN - SCOPUS:105030652023

SN - 9783032163882

T3 - Lecture Notes in Networks and Systems

SP - 256

EP - 265

BT - International Conference on Sustainability and Innovation Processes and Systems - SUSTAIN Bahrain 2025

A2 - Cantafio, Giuseppe

A2 - Alali, Layla

A2 - Maqsood, Shahid

A2 - Ramadan, Alaaeddine

A2 - Farooq, Mansoor

A2 - Al-Shoqran, Mohammad

A2 - Naser, Hanan

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Sustainability and Innovation Processes and Systems, ICSIPS 2025

Y2 - 23 June 2025 through 24 June 2025

ER -

Najjar LK, Elnakar H. Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes. In Cantafio G, Alali L, Maqsood S, Ramadan A, Farooq M, Al-Shoqran M, Naser H, editors, International Conference on Sustainability and Innovation Processes and Systems - SUSTAIN Bahrain 2025. Springer Science and Business Media Deutschland GmbH. 2026. p. 256-265. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-032-16389-9_23

Sustainability Trade-Offs in Machine Learning–Optimized Concrete Mixes

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this