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AI-Driven Smart Sensing and Non-Destructive Testing for Sustainable Innovation: Enhancing Environmental Sustainability and Technological Progress

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2266

Special Issue Editors

Dr. Domenico De Carlo

E-Mail Website
Guest Editor
Department DICEAM, Mediterranea University, 89124 Reggio Calabria, Italy
Interests: antennas; artificial intelligence; electromagnetic fields; sensory
Dr. Filippo Laganà

E-Mail Website
Guest Editor
Laboratory of Biomedical Applications Technologies and Sensors (BATS), Department of Health Science, Magna Græcia University, 88100 Catanzaro, Italy
Interests: electronic systems; sensor systems; digital electronics; embedded systems; thermographic analysis on biomedical and industrial applications and devices; NDT; material for biomedical applications; sensors; thermography; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue invites contributions from scholars working at the forefront of sustainable innovation, focusing on the development of methodologies, models, and systems that harness artificial intelligence (AI), non-destructive testing (NDT), and advanced sensing technologies. Our goal is to drive environmental sustainability through intelligent monitoring, predictive diagnostics, and resource-efficient solutions. We aim to collect pioneering research that demonstrates how AI-powered sensors and NDT techniques can enable early fault detection, optimize maintenance, extend the life cycle of infrastructures, and reduce environmental impact. By promoting smart, non-invasive, and energy-conscious technologies, this Special Issue seeks solutions that adhere to the UN’s Sustainable Development Goals (SDGs) and offer scalable strategies for preserving natural resources while fostering technological progress. This Special Issue will enrich the existing literature by bridging the gap between environmental sustainability and technological innovation, showcasing integrated approaches that are essential for building a greener and smarter future.

https://www.mdpi.com/journal/sustainability/about

Dr. Domenico De Carlo
Dr. Filippo Laganà
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • environmental sustainability
  • artificial intelligence for green technologies
  • non-destructive testing (NDT) and sustainability
  • smart sensors for environmental monitoring
  • sustainable infrastructure assessment
  • predictive maintenance and resource optimization
  • intelligent sensing systems for sustainability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 2357 KB  
Article
GMamba: A Lightweight Mamba Model for Garbage Classification
by Lujun Lin, Qifeng Ding, Xinzhan Li, Haoji Hu, Qun Wang and Houkui Zhou
Sustainability 2026, 18(11), 5397; https://doi.org/10.3390/su18115397 - 27 May 2026
Viewed by 175
Abstract
With the rapid increase in urban waste, efficient and accurate garbage classification has become pivotal for sustainable development. However, existing methods often grapple with high computational complexity, limited adaptability to diverse waste types, and challenges in deploying on resource-constrained devices. To address these [...] Read more.
With the rapid increase in urban waste, efficient and accurate garbage classification has become pivotal for sustainable development. However, existing methods often grapple with high computational complexity, limited adaptability to diverse waste types, and challenges in deploying on resource-constrained devices. To address these issues, this study proposes GMamba, a lightweight garbage classification model based on the Mamba architecture. GMamba employs a hierarchical structure, integrating two modules, the GML Block for efficient local–global feature fusion and the GMC Block for fine-grained spatial dependency modeling, achieving robust feature aggregation while minimizing computational redundancy. Evaluations on the Huawei Cloud Garbage Classification dataset and the custom MixTrash dataset demonstrate that GMamba, with only 17.18 M parameters, achieves Top-1 accuracies of 92.75% and 92.58%, respectively. While scaling evaluations indicate that VMamba maintains a marginal lead in absolute Top-1 accuracy, the proposed GMamba delivers a substantially superior balance between accuracy and computational efficiency, reducing parameter count by 45% and FLOPs by 47.3%, thus demonstrating promising deployment potential for resource-constrained edge systems. Full article
(This article belongs to the Special Issue AI-Driven Smart Sensing and Non-Destructive Testing for Sustainable Innovation: Enhancing Environmental Sustainability and Technological Progress)
23 pages, 1977 KB  
Article
A Generalizable Hybrid AI-LSTM Model for Energy Consumption and Decarbonization Forecasting
by Khaled M. Salem, A. O. Elgharib, Javier M. Rey-Hernández and Francisco J. Rey-Martínez
Sustainability 2025, 17(23), 10882; https://doi.org/10.3390/su172310882 - 4 Dec 2025
Cited by 5 | Viewed by 1127
Abstract
This research presents a solution to the problem of controlling the energy demand and carbon footprint of old buildings, with the focus being on a (heated) building located in Madrid, Spain. A framework that incorporates AI and advanced hybrid ensemble approaches to make [...] Read more.
This research presents a solution to the problem of controlling the energy demand and carbon footprint of old buildings, with the focus being on a (heated) building located in Madrid, Spain. A framework that incorporates AI and advanced hybrid ensemble approaches to make very accurate energy consumption predictions was developed and tested using the MATLAB environment. At first, the study evaluated six individual AI models (ANN, RF, XGBoost, RBF, Autoencoder, and Decision Tree) using a dataset of 100 points that were collected from the building’s sensors. Their performance was evaluated with high-quality data, which were ensured to be free of missing values or outliers, and they were prepared using L1/L2 normalization to guarantee optimal model performance. Later, higher accuracy was achieved through combining the models by means of hybrid ensemble techniques (voting, stacking, and blending). The main contribution is the application of a Long Short-Term Memory (LSTM) model for predicting the energy consumption of the building and, very importantly, its carbon footprint over a 30-year period until 2050. Additionally, the proposed methodology provides a structured pathway for existing buildings to progress toward nearly Zero-Energy Building (nZEB) performance by enabling more effective control of their energy demand and operational emissions. The comprehensive assessment of predictive models definitively concludes that the blended ensemble method is the most powerful and accurate forecasting tool, achieving 97% accuracy. A scenario where building heating energy use jumps to 135 by 2050 (a 35% increase above 2020 levels) represents an alarming complete failure to achieve energy efficiency and decarbonization goals, which would fundamentally jeopardize climate targets, energy security, and consumer expenditure. Full article
(This article belongs to the Special Issue AI-Driven Smart Sensing and Non-Destructive Testing for Sustainable Innovation: Enhancing Environmental Sustainability and Technological Progress)
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