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Engineering Properties and Environmental Effect of Recycled Waste in Geotechnical Engineering

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8929

Special Issue Editor

Dr. Liet Chi Dang

E-Mail Website
Guest Editor
Department of Civil Engineering, Gwangju University, 277 Hyodeok-ro, Nam-gu, Gwangju 61743, Korea
Interests: expansive soil; recycling industrial waste; pavement material; sustainable waste utilization; soil stabilization; fibre reinforced material; recycled construction material; numerical analysis; slope stability assessment; ground improvement techniques; numerical modelling in geotechnical engineering; environmental impact assessment; solid waste management

Special Issue Information

Dear Colleagues,

Due to the enormous development of agriculture and industrial production worldwide in the last few decades, a huge amount of agricultural wastes and industrial byproducts such as fly ash, bagasse ash, rice husk ash, blast furnace slag, calcium carbide residue, rubber crumb, plastic waste, fibre wastes (e.g., carpet fibre, bagasse fibre, coconut coir fibre and textile) have been generated and posed threats to human health and the environment. In general engineering practice, incinerators and landfilling are widely used as a conventional method to eliminate these types of industrial byproducts and agricultural wastes while recycling these wastes has remained limited. Therefore, while raising concerns about the environmental pollution of waste deposal, scarcity of landfill spaces, growing costs of landfilling these agro-industrial wastes, alternative options including utilising the agro-industrial wastes and byproducts as recycled construction materials, pavement materials and stabilising additives for weak ground improvement are required to effectively mitigate the harmful impacts on the environment of waste disposal while minimising construction costs of civil infrastructure projects in relation to purchasing construction materials, filling materials and conventional stabilisers such lime or cement.

This Special Issue focuses on the generation and characterisation of engineering properties, assessing the environmental impact of agro-industrial wastes and by-products and explores their potential applications as promising eco-friendly, cost-effective construction materials, green stabilisers for sustainable stabilisation of soft soils in support of the construction of civil engineering structures. Distinctive research on either partial cement replacement by agro-industrial wastes or incorporating recycled wastes into the most suitable geomaterials, pavement materials, construction and building materials is highly encouraged to accelerate the reuse of agro-industrial wastes rather than dumping at landfills for sustainability and a cleaner environment. Meanwhile, the sustainable utilisation of recycled wastes in environmental and geotechnical engineering is predicted not only to offer an effective waste management technique but also limit the carbon footprint, energy consumption by cement production and its consumption in improving the geotechnical engineering properties of weak grounds and soft rocks used as stiff soil foundations for the construction of civil infrastructures and buildings.

In this Special Issue, original research articles and reviews addressing the environmental impact of agro-industrial waste disposal, characterising engineering properties of recycled wastes and their applications in geotechnical engineering are welcome. Research areas may include (but are not limited to) the following:

  • Assessing the environmental impact of waste disposal
  • Soil stabilisation by recycled wastes
  • Numerical modelling and machine learning of waste contamination
  • Experimental investigations of utilising recycled wastes in geotechnical engineering
  • Solid waste management techniques
  • Recycling wastes in pavement base and subbase
  • Recycled construction and building materials
  • Alternative additives/green chemical stabilisers for ground improvement
  • Sustainable development of civil infrastructures
  • Engineering applications of recycled waste for a cleaner environment and production

I look forward to receiving your contributions to this Special Issue.

Dr. Liet Chi Dang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • industrial waste
  • environmental impact
  • expansive soil
  • soil stabilisation
  • mechanical properties
  • recycled construction materials
  • recycled fibre waste
  • fibre reinforced material
  • geopolymer
  • waste management technique
  • sustainability

Published Papers (5 papers)

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21 pages, 4954 KiB  
Article
Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods
by Celal Cakiroglu and Gebrail Bekdaş
Sustainability 2023, 15(6), 4957; https://doi.org/10.3390/su15064957 - 10 Mar 2023
Cited by 3 | Viewed by 1106
Abstract
Construction and demolition waste (CDW) together with the pollution caused by the production of new concrete are increasingly becoming a burden on the environment. An appealing strategy from both an ecological and a financial point of view is to use construction and demolition [...] Read more.
Construction and demolition waste (CDW) together with the pollution caused by the production of new concrete are increasingly becoming a burden on the environment. An appealing strategy from both an ecological and a financial point of view is to use construction and demolition waste in the production of recycled aggregate concrete (RAC). However, past studies have shown that the currently available code provisions can be unconservative in their predictions of the shear strength of RAC beams. The current study develops accurate predictive models for the shear strength of RAC beams based on a dataset of experimental results collected from the literature. The experimental database used in this study consists of full-scale four-point flexural tests. The recycled coarse aggregate (RCA) percentage, compressive strength (fc′), effective depth (d), width of the cross-section (b), ratio of shear span to effective depth (a/d), and ratio of longitudinal reinforcement (ρw) are the input features used in the model training. It is demonstrated that the proposed machine learning models outperform the existing code equations in the prediction of shear strength. State-of-the-art metrics of accuracy, such as the coefficient of determination (R2), mean absolute error, and root mean squared error, have been utilized to quantify the performances of the ensemble machine learning models. The most accurate predictions could be obtained from the XGBoost model, with an R2 score of 0.94 on the test set. Moreover, the impact of different input features on the machine learning model predictions is explained using the SHAP algorithm. Using individual conditional expectation plots, the variation of the model predictions with respect to different input features has been visualized. Full article
(This article belongs to the Special Issue Engineering Properties and Environmental Effect of Recycled Waste in Geotechnical Engineering)
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18 pages, 6211 KiB  
Article
Investigation of Strength and Microstructural Characteristics of Blended Cement-Admixed Clay with Bottom Ash
by Chana Phutthananon, Niyawan Tippracha, Pornkasem Jongpradist, Jukkrawut Tunsakul, Weerachart Tangchirapat and Pitthaya Jamsawang
Sustainability 2023, 15(4), 3795; https://doi.org/10.3390/su15043795 - 19 Feb 2023
Cited by 3 | Viewed by 1509
Abstract
This research presents an experimental study of the strength and microstructural characteristics of cement-bottom ash-admixed Bangkok clay, paying special attention to the efficiency of adding up the bottom ash (BA) of different finesses as a cementitious material and the role played by BA [...] Read more.
This research presents an experimental study of the strength and microstructural characteristics of cement-bottom ash-admixed Bangkok clay, paying special attention to the efficiency of adding up the bottom ash (BA) of different finesses as a cementitious material and the role played by BA in enhancing the strength of the mixture. The obtained results were discussed with cemented clay mixed with other industrial ashes (i.e., fly ash and risk husk ash). The pozzolanic reaction and packing effect of BA on strength development were also discussed with tests of mixtures with insoluble material. The experimental study was performed through unconfined compression (UC), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) tests. The obtained results demonstrate that the BA could be advantageously supplemented as cementitious material into the cement-admixed clay mixture to improve the strength characteristic. The finer particle size of BA could be beneficial for achieving a high strength due to the pozzolanic reaction and packing effects. By adding up a BA content of larger than 15% when the base cement content is not less than 20%, the strength of the mixture increased efficiently with the increasing BA content. Compared with fly ash of a similar grain size, the higher efficiency of BA is obtained when a BA content of greater than 15% is considered. Finally, the microstructure and changes in elemental composition/distribution were analyzed by TGA and SEM tests to explain the mechanism to improve the strength of cement–BA-admixed clay. Full article
(This article belongs to the Special Issue Engineering Properties and Environmental Effect of Recycled Waste in Geotechnical Engineering)
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29 pages, 24206 KiB  
Article
Response Surface Methodology: The Improvement of Tropical Residual Soil Mechanical Properties Utilizing Calcined Seashell Powder and Treated Coir Fibre
by Vivi Anggraini, Sandun Dassanayake, Endene Emmanuel, Lee Li Yong, Fatin Amirah Kamaruddin and Agusril Syamsir
Sustainability 2023, 15(4), 3588; https://doi.org/10.3390/su15043588 - 15 Feb 2023
Cited by 1 | Viewed by 1456
Abstract
Calcined seashell (CSS) powder and treated coir fibre (CF) are well-established additives for reinforcing poor soils. However, the absence of specific mix designs to optimize the mix additives makes it difficult to predict their combined effect on improving the mechanical behaviour of poor [...] Read more.
Calcined seashell (CSS) powder and treated coir fibre (CF) are well-established additives for reinforcing poor soils. However, the absence of specific mix designs to optimize the mix additives makes it difficult to predict their combined effect on improving the mechanical behaviour of poor soils. This research explores the use of response surface methods to find the optimal proportions of CSS and CF for enhancing the mechanical properties of a tropical residual soil. This study uses a combination of Analysis of Variance (ANOVA) and regression models to examine how the independent variables of the CSS content, CF content, and curing duration influence the responses of the Unconfined Compressive Strength (UCS), Flexural Strength (FS), and Indirect Tensile Strength (ITS). The findings show that the optimal mix of 9.06% CSS, 0.30% CF, and 12 days of curing significantly improved the UCS, FS, and ITS by roughly six, four, and three times, respectively. Microstructural analysis revealed that the formation of calcium-aluminate-hydrate and calcium-silicate-hydrate are the primary components responsible for the enhanced mechanical properties of the treated soil. Full article
(This article belongs to the Special Issue Engineering Properties and Environmental Effect of Recycled Waste in Geotechnical Engineering)
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22 pages, 7528 KiB  
Article
Using Artificial Intelligence Approach for Investigating and Predicting Yield Stress of Cemented Paste Backfill
by Van Quan Tran
Sustainability 2023, 15(4), 2892; https://doi.org/10.3390/su15042892 - 06 Feb 2023
Cited by 6 | Viewed by 1229
Abstract
The technology known as cemented paste backfill (CPB) has gained considerable popularity worldwide. Yield stress (YS) is a significant factor considered in the assessment of CPB’s flowability or transportability. The minimal shear stress necessary to start the flow is known as Yield stress [...] Read more.
The technology known as cemented paste backfill (CPB) has gained considerable popularity worldwide. Yield stress (YS) is a significant factor considered in the assessment of CPB’s flowability or transportability. The minimal shear stress necessary to start the flow is known as Yield stress (YS), and it serves as an excellent measure of the strength of the particle-particle interaction. The traditional evaluation and measurement of YS performed by experimental tests are time-consuming and costly, which induces delays in construction projects. Moreover, the YS of CPB depends on numerous factors such as cement/tailing ratio, solid content and oxide content of tailing. Therefore, in order to simplify YS estimation and evaluation, the Artificial Intelligence (AI) approaches including eight Machine Learning techniques such as the Extreme Gradient Boosting algorithm, Gradient Boosting algorithm, Random Forest algorithm, Decision Trees, K-Nearest Neighbor, Support Vector Machine, Multivariate Adaptive Regression Splines and Gaussian Process are used to build the soft-computing model in predicting the YS of CPB. The performance of these models is evaluated by three metrics coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The 3 best models were found to predict the Yield Stress of CPB (Gradient Boosting (GB), Extreme Gradient Boosting (XGB) and Random Forest (RF), respectively) with the 3 metrics of the three models, respectively, GB {R2 = 0.9811, RMSE = 0.1327 MPa, MAE = 0.0896 MPa}, XGB {R2 = 0.9034, RMSE = 0.3004 MPa, MAE = 0.1696 MPa} and RF {R2 = 0.8534, RMSE = 0.3700 MPa, MAE = 0.1786 MPa}, for the testing dataset. Based on the best performance model including GB, XG and RF, the other AI techniques such as SHapley Additive exPlanations (SHAP), Permutation Importance, and Individual Conditional Expectation (ICE) are also used for evaluating the factor effect on the YS of CPB. The results of this investigation can help the engineers to accelerate the mixed design of CPB. Full article
(This article belongs to the Special Issue Engineering Properties and Environmental Effect of Recycled Waste in Geotechnical Engineering)
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37 pages, 5813 KiB  
Systematic Review
A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials
by Ahmed Hassan Saad, Haslinda Nahazanan, Badronnisa Yusuf, Siti Fauziah Toha, Ahmed Alnuaim, Ahmed El-Mouchi, Mohamed Elseknidy and Angham Ali Mohammed
Sustainability 2023, 15(12), 9738; https://doi.org/10.3390/su15129738 - 19 Jun 2023
Cited by 3 | Viewed by 2179
Abstract
According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at [...] Read more.
According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques. Full article
(This article belongs to the Special Issue Engineering Properties and Environmental Effect of Recycled Waste in Geotechnical Engineering)
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