Incineration of municipal solid waste is increasingly being adopted in developing countries from the past couple of decades as a waste management strategy. This is driven by rapid urbanization, population growth and scarcity of land for landfills. MSW incineration reduces the volume of waste by 70–90% but still leaves behind substantial quantities of residual ash for disposal. Managing and disposing ash safely adds significant costs over just burning the waste, undermining the economics of incineration. The present study explores the potential applications of municipal solid waste incinerated (MSWI) ash as a landfill liner. This study provides a comprehensive characterization of both fresh and aged incinerated MSW ash (fly ash and bottom ash) collected from waste to energy plant (WTE). Analyzing the physicochemical properties of incinerated ash incorporating its mineralogy, morphology, and chemical composition is essential for its effective application in geotechnical engineering. This approach offers a sustainable alternative to traditional liner materials.

Incineration of municipal solid waste (MSW) along with energy recovery has been proven to reduce the volume of waste destined for landfills by as much as 90%. According to Indian solid waste management regulations, all municipal solid waste must be treated in composting facilities, waste-to-energy facilities, or other processing plants before being disposed of in landfills. This requirement not only lessens the spatial footprint of landfills but also contributes positively toward environmental sustainability. However, one challenge that remains is how to effectively reuse or dispose of the residues left behind after the incineration process. Even with comprehensive resource recovery, current estimates indicate that 25–35% of total MSW generated remains as residue that accumulates in landfills if not further used. Therefore, this research focuses on exploring the potential application of incinerated MSW ash as landfill liners. The study undertakes a meticulous analysis of both bottom ash and fly ash through extensive geotechnical characterizations. In order to gauge their suitability as landfill liners, the study conducts detailed geotechnical analysis on various aspects such as hydraulic conductivity and compressibility. Ultimately, this research promotes the massive utility of incinerated MSW ash, thereby encouraging a sustainable approach toward landfill management.

Municipal solid waste landfills require liner systems to prevent leachate migration into the environment. Liner selection typically focuses on engineering performance, cost, and ease of construction, with limited emphasis on sustainability. This study assessed the sustainability of four composite liner systems using the triple bottom line approach, considering environmental, economic, and social impacts, along with technical equivalence based on leachate infiltration rates. The four systems analyzed were: (1) geomembrane (GM) over compacted clay liner (CCL) (GM/CCL), (2) GM over geosynthetic clay liner (GCL) (GM/GCL), (3) GM over soil mixed with lime and cement (SA) (GM/SA), and (4) GM over fly ash mixed with bentonite (FAB) (GM/FAB). Life cycle stages-material extraction, construction, monitoring, and disposal-were evaluated. The study focused on DeKalb County Landfill in DeKalb, Illinois, USA, with environmental impacts quantified using the Eco-Indicator 99 and TRACI methods in SimaPro 8.0.1. Results showed that GM/FAB was the most sustainable liner in terms of environmental impact and second in economic and social considerations. However, GM/GCL was the most preferred based on economic and social impacts.

Municipal solid waste (MSW) is typically composed of organic and inorganic constituents that can decompose and release substantial amounts of phosphate into the environment, while impoundments contain the same phosphate-contaminated leachate. Stormwater retention ponds, on the other hand, have high concentrations of phosphate resulting from surface runoff. Infiltration of these waste liquids into the subsurface can contaminate the groundwater which necessitates the use of engineered liners to prevent such conditions. Compacted clay and geosynthetic clay liners are commonly used liners in these waste containment systems, but availability of these materials in remote areas of developing countries is challenging. This study proposes using bentonite amended fly ash as a potential sustainable alternative liner. Fly ash is a locally available by-product of coal combustion at power plants, and use of this will prevent its disposal and utilize it as a useful resource material. Preliminary studies showed 80% fly ash and 20% bentonite mix proportion is optimal to provide required hydraulic conductivity. The present study reports laboratory testing to investigate phosphate retention at this optimal mix conditions. Batch tests are conducted using fly ash and bentonite to determine their removal potential under different phosphate concentrations. In addition, column experiments were conducted on optimal bentonite-amended fly ash to assess the hydraulic conductivity and phosphate retention potential. Overall, the test results showed that the optimized bentonite-amended fly ash will serve as an effective low permeable liner with efficient phosphate retention.

Sustainable Materials in Civil Infrastructure delves into cutting-edge advancements in eco-materials, offering solutions crucial for building resilient and sustainable infrastructure. It provides profound insights into pioneering research on eco-materials for construction, offering a comprehensive guide on recycled steel, low-carbon concrete, bioconcrete, self-healing concrete, and industrial by-products like fly ash and shape memory alloys. Chapters explore design applications of bioconcrete and the utilization of eco-materials in landfill liners and masonry, while also addressing obstacles hindering the widespread adoption of green concrete and bioconcrete, proposing practical solutions. This book serves as a cornerstone for the development of sustainable design methodologies, embraced by environmental monitoring bodies worldwide.

Geomembrane (GM) liners anchored in the trenches of municipal solid waste (MSW) landfills undergo pull-out failure when the applied tensile stresses exceed the ultimate strength of the liner. The present study estimates the tensile strength of GM liner against pull-out failure from anchorage with the help of machine-learning (ML) techniques. Five ML models, namely multilayer perceptron (MLP), extreme gradient boosting (XGB), support vector regression (SVR), random forest (RF) and locally weighted regression (LWR) were employed in this work. The effect of anchorage geometry, soil density and interface friction were studied with regards to the tensile strength of the GM. In this study, 1520 samples of soil–GM interface friction were used. The ML models were trained and tested with 90% and 10% of data, respectively. The performance of ML models was statistically examined using the coefficients of determination (R2, R2adj) and mean square errors (MSE, RMSE). In addition, an external validation model and K-fold cross-validation techniques were used to check the models’ performance and accuracy. Among the chosen ML models, MLP was found to be superior in accurately predicting the tensile strength of GM liner. The developed methodology is useful for tensile strength estimation and can be beneficially employed in landfill design.

This study employs machine learning (ML) techniques and artificial neural networks (ANN) to predict the interface friction angle between the landfill liner and the soil. The interface behavior is majorly affected by the thickness of landfill liner (t), mass of landfill liner (m), tensile strength of landfill liner (T), cohesion of soil (cu), angle of shearing resistance of soil (Φ), shear strength (τ), and normal stress (σ). As the stability of landfill liner varies significantly from that of the soil due to the non-homogeneity and anisotropic character of the soil, it is critical to comprehend the interface behavior between the landfill liner and the soil. However, no prior research employing machine learning techniques to analyze the interface behavior between landfill liners and soil has been reported; a study using machine learning algorithms and artificial neural networks is carried out on 66 datasets to probe the interface behavior with the help of an ANACONDA navigator. Further, to understand the impact of input variables on the output variable, Pearson’s correlation coefficients were determined. Mean absolute error (MEA) is considered as a loss function, and the best model was chosen based on the r2-value. Random forest regressor (RFR) model is determined to be the best model among the available models with an r2-score of 0.99 and a minimum mean absolute error of 0.46.

Soil and groundwater contamination is caused by improper waste disposal practices and accidental spills, posing threat to public health and the environment. It is imperative to assess and remediate these contaminated sites to protect public health and the environment as well as to assure sustainable development. Site remediation is inherently complex due to the many variables involved, such as contamination chemistry, fate and transport, geology, and hydrogeology. The selection of remediation method also depends on the contaminant type and distribution and subsurface soil and groundwater conditions. Depending on the type of remediation method, many systems and operating variables can affect the remedial efficiency. The design and implementation of site remediation can be expensive, time-consuming, and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have the potential to make site remediation cost-effective with reduced human effort. This study provides a brief overview of these emerging technologies and presents case studies demonstrating how these technologies can help contaminated site remediation decisions.

Coal mine waste rock is generated during coal extraction and is usually disposed of in non-engineered dumps. The dumps are extended vertically to 100–120 m height to reduce the spatial footprint. The waste mass generally consists of loose, cohesionless material associated with high heterogeneity, so the dumps are prone to slope failures. A typical dump configuration in Jharkhand, India (total height, H = 125 m; slope angle, θ = 3V:1H) is considered for evaluation in this study. A 2D limit equilibrium numerical analysis is performed to estimate the slope stability. A parametric study is conducted to understand the effect of bench height (H), bench width (W), and slope angle (θ) on the factor of safety. The heterogeneity of the material is analyzed using the probabilistic descriptors (mean, standard deviation, and coefficient of variation (CoV)). The influence of CoV on the shear strength parameters is studied at various intervals ranging from 10 to 80% and compared using Monte Carlo simulation and alternative point estimate methods. Further, the modified slope geometry and the benches are recommended as remediation methods to achieve the desired safety factor. The results provide valuable insights into understanding the influence of slope geometry and material heterogeneity during the stability analysis of coal mine dumps.

The growing number of contaminated sites across the world pose a considerable threat to the environment and human health. Remediating such sites is a cumbersome process with the complexity originating from the need for extensive sampling and testing during site characterization. Selection and design of remediation technology is further complicated by the uncertainties surrounding contaminant attributes, concentration, as well as soil and groundwater properties, which influence the remediation efficiency. Additionally, challenges emerge in identifying contamination sources and monitoring the affected area. Often, these problems are overly simplified, and the data gathered is underutilized rendering the remediation process inefficient. The potential of artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) to address these issues is noteworthy, as their emergence revolutionized the process of data management/analysis. Researchers across the world are increasingly leveraging AI/ML/DL to address remediation challenges. Current study aims to perform a comprehensive literature review on the integration of AI/ML/DL tools into contaminated site remediation. A brief introduction to various emerging and existing AI/ML/DL technologies is presented, followed by a comprehensive literature review. In essence, ML/DL based predictive models can facilitate a thorough understanding of contamination patterns, reducing the need for extensive soil and groundwater sampling. Additionally, AI/ML/DL algorithms can play a pivotal role in identifying optimal remediation strategies by analyzing historical data, simulating scenarios through surrogate models, parameter-optimization using nature inspired algorithms, and enhancing decision-making with AI-based tools. Overall, with supportive measures like open-data policies and data integration, AI/ML/DL possess the potential to revolutionize the practice of contaminated site remediation.

A novel statistical framework was developed to quantify the variability of the fitting parameters of the soil-water characteristic curve (SWCC). Reliable estimation of the mean, standard deviation, and associated probability density function (PDF) of the fitting parameters of SWCC is an important tool for addressing reliability-based designs in unsaturated soil mechanics. The assumption of either Gaussian or lognormal distributions may not be valid for representing a high degree of variability associated with fitting parameters. This study aimed to provide the most appropriate continuous PDFs by optimizing the mean and standard deviation such that errors associated with quantile, percentile, and cumulative distribution function (CDF) were as low as possible. A total of 261 and 111 sample points were collected from the most comprehensive experimental works on the fitting parameters of SWCC for clayey and silty soils, respectively. Optimum distributions suitable to the model fitting parameters of SWCC are highly dependent on the type of soil. The most appropriate PDFs for representing the fitting parameters af, nf, and mf of Fredlund and Xing's model were gamma, Weibull, and inverse Gaussian distributions, respectively. Similarly, fitting parameters af, nf, and mf for silty soils were represented by inverse Gaussian, Gumbel maximum, and Gumbel maximum distributions, respectively. This study found that the selection of inappropriate PDFs overestimated the probability of failure of unsaturated infinite slopes considerably, by 99.49% and 99.76%, respectively, for clayey and silty soils. Recommended mean, coefficient of variation (COV), and PDF are useful in the reliability-based design of unsaturated soil slopes and in judging the performance of existing infinite and finite slopes.

The safety and stability of municipal solid waste (MSW) slopes are governed by unit weight (γ), cohesion (c), and friction angle (φ) of the MSW. Variability associated with the unit weight, cohesion, and friction angle of MSW is a major problem in the design of landfills because it negatively affects the performance of the slope. Variability associated with these properties may trigger catastrophic slope failures. The reported studies on the reliability-based design of MSW landfills adopted either Gaussian or lognormal distributions based on a reasonable approximation. The limitations are apparent when a coefficient of variation (COV) is higher. The accuracy of reliability-based designs depends on the selection of best-fit continuous and extreme value distributions (such as Gumbel and Weibull) that can model a high degree of variability precisely. The present study has undertaken to propose a suitable statistical model that gives a better representation of variability by optimizing the statistical parameters. A high degree of variability associated with the unit weight, cohesion, and friction angle of MSW is also investigated. A novel approach is proposed to determine reliable continuous probability density functions (PDFs) that can be fitted to the database consisting of 184 sample points collected from the most comprehensive experimental studies reported in the literature. The best-fit PDFs are recommended by optimizing the mean and standard deviation such that errors associated with quantiles (Q - -Q), percentiles (P-P), and cumulative distribution functions (CDFs) are as minimum as possible. This study signifies the selection of optimized PDFs for the representation of parameter variability, and it is proved that the probability of failure is either underestimated or overestimated considerably when other conventional PDFs are chosen. The recommended mean, COV, and PDF can be useful in the reliability-based design of engineered MSW landfills and for judging the performance of existing MSW slopes.

In this paper, a numerical analysis is presented using fast Lagrangian analysis of continua (FLAC) 2D for the evaluation of slope reliability. The Cholesky decomposition technique has been used to develop the 2D non-Gaussian homogeneous random field. The performance function against MSW slope failure is formulated based on the linear regression equation of factor of safety obtained using numerical package, FLAC 2D. A reliability index of MSW slopes is computed using first order reliability method (FORM). The spatially variable design parameters of MSW landfill slopes are treated as random variables. The results of the present study provide an ample understanding of the spatial variability associated with shear parameters of MSW landfill slopes. The shear stress and shear displacements along the geomembranes (GMB) interfaces are presented with and without spatial variability. The effect of correlation distance on the reliability index of MSW landfill slope is presented in the form of design charts for different mean and COV values associated with unit weight, cohesion, and friction angle of the MSW.

Expansive soils often exhibit volumetric changes with variations in the seasonal moisture. Pavements constructed on expansive soil subgrades may subject to cracking, waviness, and distress. This paper reports the laboratory CBR and UCS test results conducted on expansive soils treated with phosphogypsum (PG) and crumb waste rubber (CRW). The influence of several mix proportions of PG-CRW on the strength properties of expansive soil was studied through experimentation. A significant improvement was observed in the CBR and UCS values, at optimum percentages of 6% and 2% respectively for PG and CRW. Further, the durability of the proposed mix proportions was interpreted from the soil samples subjected to curing for 28 days. An attempt was made to correlate the CBR and UCS values of the treated soil matrix through linear regression analysis. The results of the study can be gainfully utilized in the design pavement subgrades on soft soils.

In this paper a new control algorithm has been proposed to control the power flow of the utility grid which has been penetrated by renewable energy sources (RES). The proposed control algorithm ensures utility grid to undergo minimum power changes. The hybrid energy storage system consisting of high power density element such as super capacitor and high energy density element such as battery storage has been used to compensate the residual power. The proposed control algorithm has been implemented to the distribution static compensator (DSTATCOM) to achieve multiple goals, such as integrating photo voltaic (PV) to the utility grid, compensating the reactive power and current related power quality issues and the power flow balancing. The balancing of the energy flows and current related power quality issues have been compensated using a DSTATCOM. The proposed configuration of the system has been implemented in MATLAB/Simulink and the corresponding simulation results are being presented in the manuscript.

Geomembrane liners that are installed in anchor trenches may experience pullout failure when the applied tensile stress exceeds the allowable strength of the liner. Deterministic analysis approaches do not consider the variability of the unit weight and friction angle of the soil, interface friction between the geomembrane and the cover soil, and tensile strength of the geomembrane. An analytical expression based on the Euler-Eytelwein equation is derived for the mobilized tension in the anchor. This paper proposes a new procedure for the target reliability-based design optimization (TRBDO) of V-shaped anchor trenches. The effect of the bend resistance on the GMB tensile force and reliability index is discussed. This approach is used to determine an optimal allowable geomembrane tensile force required to avoid pullout failure such that the prescribed reliability indices are attained in the presence of parameter variability. The optimization methodology is useful to develop modifications for conventional analytical models in practice. Thus, the proposed procedure combines modern concepts of reliability analysis, anchor trench design, and nonlinear constrained optimization to develop a rational and practical procedure for the optimal design of V-shaped anchor trenches.

The main objective of design of V-shaped anchor trenches is to ensure safety against the pullout failure in Geomembrane (GMB) liners efficiently. Uncertainties related to soil-liner interface frictional angle, allowable GMB tensile force, and the unit weight of the cover/backfill soil can be quantified through probabilistic means. Load and Resistance Factor Design approach involves reliability theory in the evaluation of load and resistance factors. The present study focuses on the application of reliability based load and resistance factor design of V-shaped anchor trenches. This paper gives a clear guideline for the successful performance of anchor trenches against pullout failure in handling the variability of soil-liner interface frictional angle, allowable GMB tensile force, and the unit weight of the cover/backfill soil. Target reliability approach is used to estimate the probability of pullout failure of GMB liner. The results of the study can be used to understand the response of the anchor trench for variable loads. The study recommends the resistance and the load factors for the design of V-shaped anchor trenches of MSW Landfills.

The requisite for a careful design of pavement subgrades and subbases has been stressed by the failures caused by fallacy in understanding the variability and uncertainty associated with material properties (Jung et al. 2012). This study emphasizes the improvement of California bearing ratio (CBR) found in the expansive soil after treating with lime and fly ash through probabilistic evaluation. The variability associated with the CBR values is studied for twenty soil specimens stabilized with lime and fly ash at varying proportions. A comprehensive analysis has been carried out in a probabilistic framework for a complete understanding of the variability range. The aftermath of the investigation can be suitably beneficial for a reliable and reasonably economical design of pavement subgrades.

This paper presents the target reliability based design framework for V-shaped anchored trenches to resist the sliding action and pull-out of geosynthetic liners. V-shaped anchor trenches are subject to various uncertainties arising from inconsistency in parameter estimation, improper compaction of cover/backfilled soils, field construction practices, the assumption of relative slippage at soil-liner interface and invention of new design methods. Therefore, based on the compilation of interface friction angles between different types of geomembranes (GMB) and granular soils according to several published studies, the mean, standard deviation, and coefficient of variation are computed using statistical analysis. Moreover, a framework for reliability based design is presented by considering the unit weights of backfill and cover soil, interface friction angle and allowable GMB tensile force. The optimum allowable GMB tensile force needed to maintain the stability against pull-out failure by targeting various reliability indices is obtained for various design parameters. The obtained results highlight the potential benefits of reliability methods and encourage their implementation during the design of MSW landfills.

Anchor trenches for MSW landfills subject to various uncertainties arising from inconsistency in parameter measurement and determination, placement of fresh waste on the geomembrane (GM) liner, improper compaction of cover and backfilled soils, construction practices, assumption of relative slippage, and invention of new design methods. A wide range of variability is associated with the design parameters of the anchorage. Conventional factor of safety approach cannot account for the uncertainties involved. The drawback can be well addressed through reliability based approaches. The objective of the analysis discussed herein is to produce estimates of the probability of anchor trench failure as opposed to the conventional factor of safety. Therefore, a framework for the target reliability based design optimization (TRBDO) methodology is presented for the design of anchor trenches. This paper emphasizes the importance of allowable design strength of the liner considering the variability for rectangular and L-shaped rectangular anchor trenches.

The shear strength properties of municipal solid waste (MSW) are of special importance when evaluating the stability of landfill slopes. Geoenvironmental engineers are well aware of the existence of many sources of uncertainties associated with shear strength parameters of MSW due to various reasons. The significant uncertainties associated with the shear strength and shear stresses render deterministic modeling potentially misleading. The traditional engineering approaches like method of slices used for evaluating MSW slopes are frequently questionable as they do not adequately account for uncertainties included in analytical modeling and natural variability. In order to quantify the slope stability precisely by taking into account the variability, the reliability based design optimization (RBDO) framework is presented. The mean and standard deviations associated with unit weight, cohesion and angle of internal friction of the MSW are taken into account in the probabilistic optimization. Reliability analysis is performed using first order reliability method (FORM). A limit state function is formulated against sliding slope failure using Spencer method of slices. The influence of coefficients of variation (COV) of stability number and friction angle on critical center coordinates and reliability index is presented in the form of charts.