GX & Decarbonization Research

M. S. Deore, D. P. D. Nemade

This study evaluates geopolymer concrete (GPC) using fly ash and GGBS, incorporating AI modeling to compare mechanical, durability, and environmental performance against conventional concrete. Findings show significant CO2 reduction and lif…

Arthur J. Sterling, Marcus P. Thorne, Julian T. Harrow

This paper integrates Lifecycle Assessment with a Multi-Objective Optimization model using a genetic algorithm to select sustainable construction materials. Applied to a mid-rise commercial building, it simultaneously minimizes lifecycle co…

M. Al-Jamal, Ayooub Alsarhan, Wafa' Q. Al-Jamal +4

This study presents a digital-twin-ready decision-support framework integrating BIM and AI-enhanced LCA. Machine learning surrogate models (Random Forest, Gradient Boosting, ANN) predict mechanical performance and lifecycle indicators (CO2,…

Y. Yardımcı, Yasemin Erbil

This review analyzes AI-integrated LCA research in construction. ML and ANN are used to predict energy and carbon, but integration is fragmented due to unstructured data and lack of standards. Focus is on operational energy, neglecting embo…

F. Pacheco-Torgal

This paper systematically reviews the role of AI in decarbonizing and enhancing resilience of the built environment. It maps AI applications across the building lifecycle—including generative design, predictive maintenance, digital twins, a…

Anna Szewczyk, J. Dzwierzynska

This systematic review evaluates how AI, Generative Design, and BIM contribute to sustainable development in construction. Using the PRISMA protocol, it synthesizes evidence on algorithmic intelligence supporting UN SDGs (9, 11, 12, 13). Fi…

Linda Cusumano, Mats Granath, N. Olsson +1

This study integrates set-based design with genetic algorithm (NSGA-II) optimization to simultaneously minimize cost and embodied carbon in early building design. Applied to a reference building, the genetic algorithm alone reduced carbon b…

Chanhyeok Kang, Bokyung Jung, Taekyu Lee +2

This study proposes a practical framework for estimating embodied carbon in buildings using automated IFC model generation and machine learning correction. With minimal inputs (gross floor area, floors, etc.), baseline emissions are compute…

A. Nawarathna, Zaid Alwan, Barry J. Gledson +1

This study develops a multiple linear regression model for early-stage embodied carbon (EC) estimation using data from 25 office buildings in Sri Lanka. Gross internal floor area (GIFA) and external wall area (EWA) are the most influential …

N. Albelwi

This paper introduces FedCarbon, a federated learning-based building stock modeling system that enables collaborative embodied carbon benchmarking without central data aggregation. Using hierarchical federated aggregation with attention-bas…

N. Albelwi

This paper proposes DT-LCAF, a digital twin-enabled LCA framework for real-time embodied carbon optimization in construction. It integrates BIM, IoT, and ML (graph attention networks and reinforcement learning), validated on proxy data from…

S. Ren, Min Chen

This study proposes a data-driven scheduling strategy for the Ship Integrated Energy System (SIES). Using LSTM for fuel consumption prediction and NSGA-II for multi-objective optimization, it simultaneously reduces CO2 emissions and costs. …

Zhihan Qiu

This paper examines the application of digital technologies (IoT, big data, AI) in ship energy efficiency management to comply with IMO regulations like CII. It analyzes practices in fuel monitoring, route optimization, and power control, i…

Raoua NACEIRI MRABTI

This paper applies AI-based optimization to green hydrogen export corridors from Morocco to Europe. Using machine learning on simulated data for road, pipeline, and sea transport, it finds that trans-Mediterranean pipelines are the most cos…

Bin Xu, Luyang Wang, Tingting Xiang +1

This study proposes a machine learning-enhanced tri-objective optimization framework for stowage planning of finished steel maritime logistics. It simultaneously maximizes deadweight utilization and minimizes carbon emissions, achieving 99.…

Shiwei Zhou, Xinglong Liu, Feng Zhang +1

This paper proposes a genetic algorithm (GA)-based ship path optimization framework that uses AIS data to incorporate wind speed and ocean currents, aiming to minimize fuel consumption and carbon emissions. Evaluated on real AIS datasets, i…

Aniruddha Bora, J. Chalfant, C. Chryssostomidis

International shipping accounts for ~3% of global GHG emissions, but routing remains heuristic. This paper presents PIER, an offline reinforcement learning framework integrating physics-informed state construction, that reduces mean CO2 emi…

Doru Coșofreț, Florin Postolache, Adrian Popa +2

This paper proposes a hybrid digital CO2 emission-control technology for maritime transport using physics-informed adaptive speed optimization. It integrates exact optimization (Backtracking, Dynamic Programming) with reinforcement learning…

Juei-Cheng Jao, Muhammad Hanzla Alvi

This paper examines legal frameworks for Maritime Autonomous Surface Ships (MASS) in the context of maritime decarbonization. It argues that existing conventions designed for crewed vessels create gaps in safety, cybersecurity, and liabilit…

M. Spalding

This paper comprehensively examines AI's role in maritime decarbonization and ocean conservation. It analyzes applications in voyage optimization, wind-assisted propulsion, vessel automation, port coordination, predictive maintenance, ship …