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Durability and Performance of Sustainable Construction and Building Materials 2nd Edition

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 845

Special Issue Editor


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Guest Editor
Expert Group Waste and Disposal, Belgian Nuclear Research Centre (SCK CEN), 2400 Mol, Belgium
Interests: Durability of concrete; hydration; microstructure; transport properties; geopolymers; waste immobilization; creep and shrinkage; carbonation; leaching; alkali silica reaction; delayed ettringite formation
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Special Issue Information

Dear Colleagues,

Construction and building materials still significantly influence the environment because of the consumption of energy and raw materials, as well as CO2 gas emissions resulting from the production of cement, which is the primary binder of concrete. The sustainable use of resources to produce more eco-friendly cementitious materials has become a trend, but is also very challenging for material engineers. With the awareness of global warming and the optimal use of resources, research on alternative binders including new cement types using secondary raw materials (e.g., composite cements, hybrid cements, and limestone-calcined clay cement) and the utilization of novel binders using industrial by-products such as geopolymers or alkali-activated materials are considered the cornerstone of sustainability in construction materials. The durability and performance of such newly developed binders in various exposure environments during service are considered to be of high interest for many applications, including classical civil engineering structures and special components for nuclear applications such as the encapsulation of radioactive waste and engineered barriers for the disposal of radioactive waste. On the one hand, durability and performance depend on exposure conditions, and on the other hand, they depend on the intrinsic properties of the materials used, including their chemistry, nano-/micro-structure and transport properties, which are still not fully understood for newly developed materials.

This Special Issue aims to reflect the current state of the art and new developments on the relevant topics in the research field of the durability and performance of classical and new binder systems. We expect a wide range of contributions from interdisciplinary, multiscale, and different approaches to addressing various durability aspects, which will provide a comprehensive background for material engineers, researchers, and experts in the field. We welcome all new ideas on various topics from young researchers as well as leading experts in the field, in the form of experimental or modelling articles, review articles, and case studies to demonstrate the advances in construction and building materials.

Dr. Quoc Tri Phung
Guest Editor

Manuscript Submission Information

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Keywords

  • durability of cement-based materials (carbonation and leaching)
  • innovative materials and their durability
  • chemical degradation (chloride and sulphate attack, alkali–silica reaction, and delayed ettringite formation)
  • interface interaction (e.g., cement–clay)
  • hydration, polymerization, and microstructure
  • transport properties (permeability and diffusion)
  • geopolymers and alkali-activated materials
  • supplementary cementitious materials
  • creep and shrinkage
  • coupled THCM
  • geochemical/reactive transport modelling
  • service life prediction
  • life cycle assessment
  • special concretes (high-performance concrete, high-strength concrete, self-compacting concrete, and recycled aggregate concrete)
  • nuclear applications of cementitious materials/alkali-activated materials (waste immobilization and irradiated concrete)

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Published Papers (1 paper)

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Research

22 pages, 3762 KiB  
Article
Comparative Analysis of Asphalt Pavement Condition Prediction Models
by Mostafa M. Radwan, Elsaid M. M. Zahran, Osama Dawoud, Ziyad Abunada and Ahmad Mousa
Sustainability 2025, 17(1), 109; https://doi.org/10.3390/su17010109 - 27 Dec 2024
Viewed by 407
Abstract
There is a growing global interest in preserving transportation infrastructure. This necessitates routine evaluation and timely maintenance of road networks. The effectiveness of pavement management systems (PMSs) heavily relies on accurate pavement deterioration models. However, there are limited comparative studies on modeling approaches [...] Read more.
There is a growing global interest in preserving transportation infrastructure. This necessitates routine evaluation and timely maintenance of road networks. The effectiveness of pavement management systems (PMSs) heavily relies on accurate pavement deterioration models. However, there are limited comparative studies on modeling approaches for rural roads in arid climatic conditions using the same datasets for training and testing. This study compares three approaches for developing a pavement condition index (PCI) model as a function of pavement age: classical regression, machine learning, and deep learning. The PCI is a pavement management index widely adopted by many road agencies. A dataset on pavement age and distress was collected over a twenty-year period to develop reliable predictive models. The results demonstrate that the regression model, machine learning model, and the deep learning model achieved a coefficient of determination (R2) of 0.973, 0.975, and 0.978, respectively. While these values are technically equal, the average bias for the deep learning model (1.14) was significantly lower than that of the other two models, signaling its superiority. Additionally, the trend predicted by the deep learning model showed more distinct phases of PCI deterioration with age than the machine learning model. The latter exhibited a wider range of PCI deterioration rates over time compared to the regression model. The deep learning model outperforms a recently developed regression model for a similar region. These findings highlight the potential of using deep learning to estimate pavement surface conditions accurately and its efficacy in capturing the PCI-age relationship. Full article
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