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Energies
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Energy-Efficient Building Materials: Innovations, Enhancements, Testing Methods, and Predictive Modelling
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Energy-Efficient Building Materials: Innovations, Enhancements, Testing Methods, and Predictive Modelling

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2006

Special Issue Editor

Prof. Dr. Barbara Klemczak

E-Mail Website
Guest Editor
Faculty of Civil Engineering, Silesian University of Technology, Akademicka 5, 44-100 Gliwice, Poland
Interests: building materials; energy-efficient sustainable materials; functional materials; cement-based materials; foam concrete; cementitious composites with waste; phase-change thermal energy storage materials; early-age concrete behaviour; testing methods; prediction models

Special Issue Information

Dear Colleagues,

The transition toward sustainable and energy-efficient construction requires building materials that not only deliver structural performance and durability but also actively contribute to reducing energy demand and environmental impact. Modern infrastructure increasingly relies on materials that combine traditional strength with enhanced thermal, mechanical, and functional properties. At the same time, the development of innovative low-carbon, waste-based, bio-based, and multifunctional energy-efficient composites offers new pathways for minimising the carbon footprint and advancing circular economy principles.

Progress in materials science and engineering—ranging from nanoscale modifications to advanced manufacturing, testing, and predictive modelling—enables the design of materials with improved energy efficiency, reliability, and adaptability. In this context, not only is knowledge about the properties of materials essential but also access to reliable testing methods and effective predictive techniques that allow for accurate assessment and modelling of their behaviour under real conditions. These approaches support performance optimisation, durability forecasting, and more informed material selection in both research and practice.

This Special Issue aims to gather recent advances in the field of future-oriented energy-efficient building materials, focusing both on the enhancement of the properties of existing materials and the development of new, sustainable solutions. Particular attention is given to innovative testing methods that are essential for reliable characterisation, as well as to predictive techniques that support accurate modelling of material behaviour. The Special Issue welcomes contributions that explore interdisciplinary approaches combining material properties, experimental techniques, and modelling strategies. The preferred types of articles are research articles, reviews, and communications. The main topics concern the performance, improvement, and predictive analysis of all energy-efficient building materials suitable for future construction challenges.

Topics of interest for publication include, but are not limited to, the following:

  • Improvements in the thermal, mechanical, and durability properties of energy-efficient building materials;
  • Development and properties of low-carbon, waste-based, and bio-based energy-efficient composites;
  • Novel and multifunctional energy-efficient building materials for structural and non-structural applications;
  • Smart materials for dynamic environmental response and self-sensing capabilities;
  • Energy-efficient and energy storage materials for building envelopes and insulation;
  • Lifecycle analysis, sustainability assessment, and circular economy integration in material design;
  • Integration of material innovations into energy-efficient and low-emission building systems.

Prof. Dr. Barbara Klemczak
Guest Editor

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. Energies 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 2600 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

  • energy-efficient building materials
  • thermal insulation materials
  • energy storage materials
  • durability forecasting and lifecycle assessment

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Published Papers (3 papers)

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Research

26 pages, 3310 KB  
Article
The Impact of ‘Thermo-Protective’ Paints on the Thermal Insulation of External Walls
by Mateusz Gawełek, Rosita Norvaisiene, Paweł Krause, Janusz Belok, Beata Wilk-Słomka, Michał Marchacz and Michał Sitek
Energies 2026, 19(10), 2362; https://doi.org/10.3390/en19102362 - 14 May 2026
Viewed by 297
Abstract
This article focuses on aspects related to the physical and thermal parameters of so-called thermal-insulating paints. These materials and systems are used in two different situations: first, as agents reducing surface temperature due to solar radiation, and second, as so-called “thermal-insulating” coatings. The [...] Read more.
This article focuses on aspects related to the physical and thermal parameters of so-called thermal-insulating paints. These materials and systems are used in two different situations: first, as agents reducing surface temperature due to solar radiation, and second, as so-called “thermal-insulating” coatings. The paper focuses on the second aspect of the applications described by the manufacturers and presents the results of the author’s laboratory tests (using an insulated heating box with two different heat sources) and field tests (in situ) on a building façade. The research methodology focuses on contact and thermal imaging measurements to assess the effectiveness and properties of reflective thermal-insulating paints, as well as analyzing their impact on the surface temperature and heat transfer coefficient of building envelopes. The conducted research showed that the use of reflective thermal-insulating paints does not significantly improve the thermal insulation of building envelopes. Measurements of the heat transfer coefficient showed a reduction of 1–7% compared to the reference wall tested. In situ measurements using temperature sensors and thermographic studies confirmed the insignificant impact of reflective thermal insulation paints on the thermal protection of external walls. Full article
(This article belongs to the Special Issue Energy-Efficient Building Materials: Innovations, Enhancements, Testing Methods, and Predictive Modelling)
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30 pages, 5223 KB  
Article
A Hybrid Framework of Quantitative Infrared Thermography and Building Energy Simulation for Cost-Optimal Building Envelope Retrofitting
by Egemen Kaymaz
Energies 2026, 19(7), 1727; https://doi.org/10.3390/en19071727 - 1 Apr 2026
Viewed by 589
Abstract
This study integrates in situ Quantitative Infrared Thermography (QIRT) and Building Energy Simulation (BES) to optimize the energy performance of an existing multi-story residential building in Istanbul, Türkiye. QIRT was utilized to diagnose thermal anomalies at the interfaces of uninsulated walls, the RC [...] Read more.
This study integrates in situ Quantitative Infrared Thermography (QIRT) and Building Energy Simulation (BES) to optimize the energy performance of an existing multi-story residential building in Istanbul, Türkiye. QIRT was utilized to diagnose thermal anomalies at the interfaces of uninsulated walls, the RC skeleton and fenestration junctions, revealing significant thermal bridging and air infiltration while enabling the calculation of the Temperature Index (TI) at critical interfaces. A key finding of the non-destructive diagnostic phase was the discrepancy between in situ (UINSITU) and theoretical (UCALC) thermal transmittance values, providing an empirical baseline for subsequent optimization. A multi-objective analysis, employing genetic algorithms (GAs), was conducted to evaluate 192 retrofit combinations, involving three insulation materials at four thicknesses and 16 glazing types. The impacts on primary energy consumption, CO2 emissions, and 30-year global costs (per EN 15459-1:2017) were quantified under volatile economic conditions. Findings indicate that the energy-optimal solution reduces primary energy by 53% and CO2 emissions by 51%, while the cost-optimal configuration reduces global costs by 52% relative to the reference case. The Pareto analysis reveals a robust convergence between financial and energy efficiency targets, proving that deep retrofitting is an economically imperative strategy for achieving national decarbonization goals and the 2053 net-zero vision. Full article
(This article belongs to the Special Issue Energy-Efficient Building Materials: Innovations, Enhancements, Testing Methods, and Predictive Modelling)
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30 pages, 4154 KB  
Article
Evaluation of Machine Learning Approaches for Hydration Heat Prediction in Energy-Efficient Cement Composites
by Barbara Klemczak, Dawid Bąba and Rafat Siddique
Energies 2026, 19(1), 39; https://doi.org/10.3390/en19010039 - 21 Dec 2025
Cited by 1 | Viewed by 624
Abstract
Accurate prediction of the heat of hydration is essential for designing low-emission, durable mortars and concretes with controlled thermal behavior, as the partial replacement of Portland cement clinker with supplementary cementitious materials (SCMs) fundamentally alters hydration kinetics. Although hydration heat can be measured [...] Read more.
Accurate prediction of the heat of hydration is essential for designing low-emission, durable mortars and concretes with controlled thermal behavior, as the partial replacement of Portland cement clinker with supplementary cementitious materials (SCMs) fundamentally alters hydration kinetics. Although hydration heat can be measured experimentally, such tests are often time-consuming and labor-intensive. Machine learning (ML)-based prediction methods offer a promising alternative, but identifying the most effective model is necessary before practical application. This study evaluates the performance of three ML algorithms, CatBoost, ExtraTrees, and XGBoost, in predicting the heat of hydration in energy-efficient cementitious composites containing SCMs. A dataset of 51 experimental samples was analyzed, comprising mix composition parameters (temperature, slag, fly ash content, and water-to-binder ratio) and four output variables: heat release rate and total heat released after 12, 72, and 168 h. Model performance was assessed using cross-validation and performance metrics (MAE, RMSE, MAPE, R2). All tested models showed a high level of fit (R2 > 0.9 for short-term predictions). ExtraTrees demonstrated the most consistent performance, particularly for hydration heat and heat rate estimation, while XGBoost showed superior accuracy for early-age heat evolution. Residual analyses confirmed model stability and minimal bias. The results indicate that ML-based modeling can significantly reduce laboratory workload and enhance understanding of hydration behavior in low-carbon cementitious systems. Full article
(This article belongs to the Special Issue Energy-Efficient Building Materials: Innovations, Enhancements, Testing Methods, and Predictive Modelling)
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