Search

Enzyme induced calcite precipitation (EICP), as a bio-cementation technique, has been used to improve the strength and stiffness of unstable soils. In this paper, the influence of key treatment conditions for EICP soil treatment such as curing temperatures (at 5°C, 25°C, 30°C and 50°C) as well as post-treatment conditions such as “oven-dried” (at 60°C) or “wet” (at room temperature, ~22°C) on the strength of EICP-treated sand were evaluated. The results from this study indicate that unconfined compressive strength (UCS), splitting tensile strength (STS), CaCO3 content (CC), the mass of precipitated CaCO3 (MCaCO3) and CaCO3 precipitation ratio (PR) increased with increasing curing temperatures during EICP soil treatment possibly due to the increase in urease enzyme activity at high temperatures. For similar CC, the “oven-dried” samples showed a significantly higher strength (i.e. UCS and STS) compared to the “wet” samples, possibly due to the transformation of unstable vaterite to a more stable form (i.e. calcite) during oven-drying. Scanning electron microscopy images and X-ray powder diffraction analysis showed that the “oven-dried” sample mainly produced calcite crystals while the “wet” sample produced a mixture of calcite and vaterite crystals. The results indicate that thermodynamically unstable vaterite crystals formed at low temperatures may be transformed into a more stable polymorph of CaCO3 (e.g. calcite) at high temperatures (~40 to 60°C), an indication of the higher strength achieved for the “oven-dried” samples

Enzyme-induced carbonate precipitation (EICP) is a bio-cementation technique and a sustainable method of ground improvement. This study examines the influence of the concentrations of substrates [S 0] and enzymes [E 0] as well as enzyme activity (A E) on the calcium carbonate (CaCO3) precipitation ratio (PR) using 130 test-tube experiments. It was found that the effect of enzyme concentration and activity on PR can be explained using a normalisation of [E s] = [E 0] × A E, where [E s] is the adjusted enzyme concentration. PR increased non-linearly with increasing [E s]/[S 0] and reached 100% at a threshold [E s]/[S 0] value of approximately 20 kU/mol. An exponential function was developed that could capture the relationship between PR and [E s]/[S 0] with reasonable accuracy. This observation was further evaluated with data from the literature consisting of a further 100 test-tube experiments. EICP solutions consisting of [E s]/[S 0] = 20 kU/mol were found to be optimum for soil treatment. The established function was later extended to predict strength gain as measured by the unconfined compressive strength (UCS) and the splitting tensile strength (STS) for EICP-treated soils and could predict the strength gain (UCS/STS) with reasonable accuracy. Results from scanning electron microscopy images, energy-dispersive X-ray spectroscopy and X-ray powder diffraction showed that the precipitated calcium carbonate in test tubes and treated soil was mostly calcite crystals with different morphologies, possibly due to the level of purity of the urease enzyme used.

Enzyme-induced carbonate precipitation (EICP) is a relatively new bio-cementation technique for ground improvement. In EICP, calcium carbonate (CaCO₃) precipitation occurs via ureahydrolysis catalysed by the urease enzyme sourced from plants. EICP offers significant potential forinnovative and sustainable engineering applications, including strengthening of soils, remediation of contaminants, enhancement of oil recovery through bio-plugging and other in situ field applications. Given the numerous potential applications of EICP, theoretical understanding of the rate and quantity of CaCO₃ precipitation via the ureolytic chemical reaction is vital for optimising the process.For instance, in a typical EICP process, the rate and quantity of CaCO3 precipitation can dependsignificantly on the concentration, activity and kinetic properties of the enzyme used along with the reaction environment such as pH and temperature. This paper reviews the research and development of enzyme-catalysed reactions and its applications for enhancing CaCO₃ precipitation in EICP. The paper also presents the assessment and estimation of kinetic parameters, such as the maximal reaction velocity (Vmax) and the Michaelis constant (Km), that are associated with applications in civil and geotechnical engineering. Various models for evaluating the kinetic reactions in EICP are presented and discussed, taking into account the influence of pH, temperature and inhibitors. It is shown that a good understanding of the kinetic properties of the urease enzyme can be useful in the development, optimisation and prediction of the rate of CaCO₃ precipitation in EICP. Refereed/Peer-reviewed

Bio-cementation, notably microbial induced calcite precipitation (MICP), and Enzyme induced calcite precipitation (EICP), binds soil particles together through calcium carbonate (CACO3) precipitation (cementation). Both MICP and EICP have significant potential for many scientific and engineering applications including improvement of strength, reducing soil liquefaction potential, surface erosion control, reducing permeability, and heavy metal contaminant remediation. However, the catalytic mechanism and the precipitation of CACO3in MICP and EICP mainly depends on the source of urease enzyme and the treatment process used. This study evaluates the mechanical and microstructural behaviour of bio-cementation using the standard MICP and EICP treatment process. The results indicate that, for the same number of treatment cycles (NTC), a higher amount of precipitated CACO3was achieved for MICP than EICP which affects the strength. For similar average CACO3content (AC), the standard treatment method used in EICP produced a higher chemical efficiency (defined as the amount of CACO3precipitated relative to the quantity of urea-CaCl2used) than in MICP. The results from scanning electron microscopy (SEM) imaging shows that the morphologies of the precipitated CACO3in MICP and EICP are similar, however, a high amount of vaterite was found in EICP than MICP. The outcome of this study indicates that the standard treatment processes used in MICP and EICP may influence the chemical efficiency, the amount, distribution, and polymorph of the precipitated CACO3which may directly affect the strength. Refereed/Peer-reviewed

Microbial-induced carbonate precipitation (MICP) and enzyme-induced carbonate precipitation (EICP) are two bio-cementation techniques, which are relatively new methods of ground improvement. While both techniques share some similarities, they can exhibit different overall behaviours due to the differences in urease enzyme sources and treatment methods. This paper presented 40 unconfined compressive strength (UCS) tests of MICP and EICP treated sand specimens with similar average calcium carbonate () content subjected to cycles of wetting-drying (WD), freezing-thawing (FT) and elevated temperature (fire resistance test – FR and thermogravimetric analysis – TG). The average content after a certain number of WD or FT cycles () and their corresponding UCS () reduced while the mass loss increased. The EICP treated sand specimens appeared to exhibit a lower resistance to WD and FT cycles than MICP treated specimens possibly due to the presence of unbonded or loosely bonded within the soil matrix, which was subsequently removed during the wetting (during WD) or thawing (during FT) process. FR test and TG analysis showed a significant loss of mass and reduction in content with increased temperatures, possibly due to the thermal decomposition of . A complete deterioration of the MICP and EICP treated sand specimens was observed for temperatures above 600 °C. The observed behaviours are complex and theoretical understanding is far behind to develop a constitutive model to predict . Therefore, a multi-objective evolutionary genetic algorithm (GA) that deals with pseudo-polynomial structures, known as evolutionary polynomial regression (EPR), was used to seek three choices from millions of polynomial models. The best EPR model produced an excellent prediction of with a minimum sum of squares error (SSE) of 2.392, mean squared error (MSE) of 0.075, root mean square error (RMSE) of 0.273 and a maximum coefficient of determination of 0.939. Refereed/Peer-reviewed

Refereed/Peer-reviewed Geological processes such as weathering and metamorphism normally lead to the destruction of primary physical features on rocks, making it difficult to accurately classify lithologies during manual core logging. As a result, manual core logging remains a subjective and inconsistent process. Machine learning techniques have shown tremendous success in lithological classification, but their performance has significant drawbacks, particularly in highly imbalanced data domains and complex geological terrain. Because there is an increasing demand for accurate and timely lithological classification, this study proposes an objective hybrid approach based on synthetic minority oversampling technique and extreme gradient boosting (SMOTE-XGB) for lithological classification within the Tarkwaian paleo placer formation using assay data obtained through X-Ray Fluorescence (XRF) analysis. Five other intelligent approaches were developed for comparative purposes to further evaluate the performance of the proposed approach, using accuracy (Acc), area under the curve (AUC), precision, sensitivity, and F1 score as evaluation metrics. The statistical results have proven that the proposed SMOTE-XGB approach is more efficient at classifying the Tarkwaian lithologies than the other machine learning models used in this study. When SMOTE was applied to the imbalanced dataset, the results revealed a significant improvement in the classification performance of the minority classes, particularly the mineralised lithologies. To that end, this research has shown that applying SMOTE to an imbalanced dataset can improve the prediction efficiency of machine learning models.

There is growing tension between high-performance machine-learning (ML) models and explainability within the scientific community. In arsenic modelling, understanding why ML models make certain predictions, for instance, “high arsenic” instead of “low arsenic”, is as important as the prediction accuracy. In response, this study aims to explain model predictions by assessing the relationship between influencing input variables, i.e., pH, turbidity (Turb), total dissolved solids (TDS), and electrical conductivity (Cond), on arsenic mobility. The two main objectives of this study are to: (i) classify arsenic concentrations in multiple water sources using novel boosting algorithms such as natural gradient boosting (NGB), categorical boosting (CATB), and adaptive boosting (ADAB) and compare them with other existing representative boosting algorithms, and (ii) introduce a novel SHapley Additive exPlanation (SHAP) approach for interpreting the performance of ML models. The outcome of this study indicates that the newly introduced boosting algorithms produced efficient performances, which are comparable to the state-of-the-art boosting algorithms and a benchmark random forest model. Interestingly, the extreme gradient boosting (XGB) proved superior over the remaining models in terms of overall and single-class performance metrics measures. Global and local interpretation (using SHAP with XGB) revealed that high pH water is highly correlated with high arsenic water and vice versa. In general, high pH, high Cond, and high TDS were found to be the potential indicators of high arsenic water sources. Conversely, low pH, low Cond, and low TDS were the main indicators of low arsenic water sources. This study provides new insights into the use of ML and explainable methods for arsenic modelling.

Refereed/Peer-reviewed Arsenic contamination is increasingly a serious global health concern, especially in developing countries like Ghana where the intensification of mining activities has resulted in adverse pollution of various water sources within mining communities. To harness the early detection of arsenic contamination in multiple water supply systems, this work aimed to assess and compare the prediction efficiency of different machine learning methods. Novel machine learning algorithms such as extreme gradient boosting (XGB), light gradient boosting (LGB) and generalized regression neural network (GRNN), which are yet to be explored in modelling arsenic concentration in various water supply systems (e.g. surface water and groundwater) were considered in this study. These models were compared with state-of-the-art machine learning methods of decision tree (DT), multivariate adaptive regression spline (MARS), multilayer perceptron neural network (MLP) and random forest (RF). The applied methods were evaluated using the coefficient of determination (R2), Nash–Sutcliff efficiency (NSE) and mean squared error (MSE). The statistical analysis showed that the newly tested models (XGB, LGB and GRNN) produced satisfactory predictions which were comparable to the state-of-the-art methods. Thus, all the methods applied including the newly introduced models have proven efficient in the arsenic modelling task with R2 > 0.93, NSE > 0.92 and MSE

Weed management and control are essential for successful crop production.In recent years, there has been increased interest in the use of sustainable biological approaches for weed control due to their potential environmental and economic benefits. In this study,the enzyme-induced carbonate precipitation (EICP )approach was adopted to form a soil crust through calcium carbonate bonding using plant-sourced urease enzymes extracted from the weed paddy melon (Cucumis myriocarpus Naud.),urea and calcium chloride solution. The penetration and erosion resistance of the EICP-treated soil crust was then measured Refereed/Peer-reviewed

Microbially induced calcium carbonate precipitation (MICP) and enzyme induced calcium carbonate precipitation (EICP) are the two most widely known bio-cementation techniques, particularly the former. EICP offers several benefits over MICP such as the smaller size of the urease enzyme making it applicable to a wider range of soils and simpler application processes. This review critically examines existing research on the EICP technique and provides an original meta-analysis of the available data. It is understood that factors such as the source and activity of the urease enzyme, the concentration of the chemical constituents (urea, calcium chloride and urease enzyme), the treatment approach used and the morphology of the precipitated calcium carbonate (CaCO3) may all affect the performance of the EICP treated material. Consequently, various enhancing additives including magnesium chloride (MgCl2), magnesium sulfate (MgSO4), zeolite, dried non-fat milk, xanthan gum, sisal fibre and cabbage juice have been utilised in recent studies to improve on the effectiveness of the EICP process. In this review, engineering parameters such as the unconfined compressive strength are correlated to the CaCO3 content (CC) and soil uniformity coefficient (Cus) for EICP treated soils using compiled data from previous literature. However, the strength of EICP-treated soil may also depend on the particular polymorph, contact location and distribution of precipitated CaCO3 as well as the soil particle size and shape. Refereed/Peer-reviewed

Monitoring of water quality through accurate predictions provides adequate information about water management. In the present study, three different modelling approaches: Gaussian process regression (GPR), backpropagation neural network (BPNN) and principal component regression (PCR) models were used to predict the total dissolved solids (TDS) as water quality indicator for the water quality management. The performance of each model was evaluated based on three different sets of inputs from groundwater (GW), surface water (SW) and drinking water (DW). The GPR, BPNN and PCR models used in this study gave an accurate prediction of the observed data (TDS) in GW, SW and DW, with the R2 consistently greater than 0.850. The GPR model gave a better prediction of TDS concentration, with an average R2, MAE and RMSE of 0.987, 4.090 and 7.910, respectively. For the BPNN, an average R2, MAE and RMSE of 0.913, 9.720 and 19.137, respectively, were achieved, while the PCR gave an average R2, MAE and RMSE of 0.888, 11.327 and 25.032, respectively. The performance of each model was assessed using efficiency based indicators such as the Nash and Sutcliffe coefficient of efficiency (ENS) and the index of agreement (d). The GPR, BPNN and PCR models, respectively, gave an ENS of (0.967, 0.915, 0.874) and d of (0.992, 0.977, 0.965). It is understood from this study that advanced machine learning approaches (e.g. GPR and BPNN) are appropriate for the prediction of water quality indices and would be useful for future prediction and management of water quality parameters of various water supply systems in mining communities where artificial intelligence technology is yet to be fully explored.

Expansive soils go through significant volume changes due to seasonal moisture variations resulting in ground movements. The ground movement related problems are likely to worsen in the future due to climate change. It is important to understand and incorporate likely future changes in design to ensure the resilience of structures built on such soils. However, there has been a limited amount of work quantifying the effect of climate change on expansive soils movement and related behaviour of structures. The Thornthwaite Moisture Index (TMI) is one of the commonly used climate classifiers in quantifying the effect of atmospheric boundary on soil behaviour. Using the long-term weather data and predicted future changes under different emission scenarios, a series of TMI maps are developed for South Australia. Potential changes in ground movement are then estimated for a selected area using a simplified methodology where the effect of future climate is captured through changes in TMI. Results indicate that South Australia is likely to face a significant reduction in TMI under all emission scenarios considered in this study. The changes in TMI will lead to a considerable increase in potential ground movement which will influence the behaviour of structures built on them and in some areas may lead to premature failure if not considered in the design Refereed/Peer-reviewed

Thesis (PhD(Civil Engineering))--University of South Australia, 2021. Includes bibliographical references (pages 351-378) Bio-cementation, notably microbial induced calcium carbonate precipitation (MICP) and enzyme induced calcium carbonate precipitation (EICP), are relatively new ground improvement methods. In this thesis, the engineering properties (strength and stiffness) of the MICP and EICP-cemented soils were assessed and compared using a series of mechanical testing and microstructural analyses. Both MICP and EICP-treated sand specimens appeared to have relatively high strength and high durability resistance. The results from the microstructural analysis indicated that both MICP and EICP produced mainly calcite crystals with minor traces of aragonite. Crude urease extract from Paddy melon seeds was found to contain a high amount of urease enzyme which could be a potential and sustainable source of enzyme for large scale applications of the EICP technique.

Enzyme induced carbonate precipitation (EICP) is a bio-cementation technique and is a new area of Geotechnical Engineering. In this paper, a parametric study was conducted to evaluate and optimize the effect of concentration and activity of urease enzyme and concentration of other chemical constituents (urea and calcium chloride) on EICP process. pH, electrical conductivity (EC), and micro-structure analysis using scanning electron microscope (SEM) imaging were performed. Results indicate that, the precipitation ratio (PR), which is defined as the ratio of mass of precipitated CaCO3 in test tube and theoretical mass of CaCO3 is a function of the concentration of urea-CaCl2 (mol/L) and urease enzyme (kU/L). Rate of change in pH and EC during urea hydrolysis is influenced by urea and urease enzyme concentration. SEM images of CaCO3 precipitates from solution containing low active urease enzyme (3,500 U/g) showed a disordered and anhedral calcite crystals whereas the high active urease enzyme (40,150 U/g) showed euhedral and agglomerated rhombohedral calcite morphology. Refereed/Peer-reviewed

The overall effectiveness of bio-cementation techniques such as microbial-induced carbonate precipitation (MICP) or enzyme-induced carbonate precipitation (EICP) can be different due to different sources of urease enzyme and treatment approach used. This paper compares the behaviour of oven-dried MICP and EICP-treated sand from macro- and micro-mechanical point of view with the number of treatment cycles and average calcium carbonate (CaCO3) content used as a comparison basis. The results indicate that in both processes, the calcium carbonate content increased with the number of treatment cycles and led to an improvement in strength (unconfined compressive and splitting tensile strength) and stiffness. For similar average calcium carbonate content, EICP-treated samples showed significantly higher splitting tensile strength (compared to MICP) even though a slightly smaller amount of precipitates were observed at particle contacts through scanning electron microscopy. This indicates, besides the average calcium carbonate content, its distribution along the height of the sample is likely to have a significant contribution towards the strength. X-ray powder diffraction and energy-dispersive X-ray spectroscopy analyses confirmed that precipitated calcium carbonate in both types of treatments were mainly calcite crystals with minor traces of aragonite.

usc Certain parts of Ghana (Eastern and Volta region) are predominantly covered by neoproterozoic Dahomeyide rocks which occurred during the Pan-African Orogeny event in West Africa. To understand the petrological, geochemical and structural evidence of this geologic event, a detailed geological mapping exercise was conducted in some parts of the Eastern and Volta regions of Ghana. The aim of this research is to understand the local geology of these areas and how it best fits the regional geology of Ghana and its implications in the Pan-African Orogenic event. The evidences presented in this study show that the rocks in these areas are mainly metamorphic, which are granitic augen gneiss, biotite gneiss and quartzite. The gneisses were proposed to belong to the basement Dahomeyan, while the quartzites belong to the Togo Structural Unit. It was also inferred that the rocks have been deformed mainly by brittle and ductile deformation, resulting in structures such as foliations, fractures and joints observed in the quartzites and gneisses within the area of study. The structures within the study area have a general attitude of north-east–south-west with some few structures trending in the north-west–south-east directions. Again, it was inferred that the rocks from the Dahomeyan structural unit are highly metamorphosed and deformed as compared to the rocks from the Togo structural unit. The granitic augen gneiss and biotite gneiss have rock-forming minerals such as biotite, K-feldspar, quartz, muscovite, plagioclase and hornblende in varying proportions, whereas the quartzites are predominantly composed of glassy quartz crystals. Refereed/Peer-reviewed

usc Flood is one of the most imperious natural hazards that can cause economic damages to infrastructures and natural ecosystems. In view of the extensive mining activities in the Tarkwa area, there has been an increase in human intervention of natural water bodies via degradation of vegetation in the watershed area, inappropriate land-use change, soil erosion and expansion of paved surface area for settlement and industrialisation. This has increased the degree of flood susceptibility within the area, with major flood incidents recorded on a timely basis. In this study, a GIS-based approach was utilised to delineate flood-prone zone within the Tarkwa mining area using Digital Elevation Models (DEMs). A DEM-derived morphologic features including local slope and streamflow network were developed using ArcGIS. The flood susceptible map was generated by merging the slope map and the stream network maps. The results indicate that about 42.59% of the study area were highly prone to flooding. The areas susceptible to flooding were mainly made up of agricultural lands with high usage of chemical fertilizers for farming, which may leach into nearby rivers, streams, lakes and groundwater during flood occurrence. It is understood from this study that the mining areas within the Tarkwa Nsuaem municipality were less susceptible to flooding. The outcome of this study provides a simplified method of mapping and assessment of flood susceptible areas which can be used as a reference for flood risk management, prevention, and reduction of natural disasters in the study area. Refereed/Peer-reviewed

Microbial-induced calcite precipitation (MICP) is a promising new technology in the area of Civil Engineering with potential to become a cost-effective, environmentally friendly and sustainable solution to many problems such as ground improvement, liquefaction remediation, enhancing properties of concrete and so forth. This paper reviews the research and developments over the past 25 years since the first reported application of MICP in 1995. Historical developments in the area, the biological processes involved, the behaviour of improved soils, developments in modelling the behaviour of treated soil and the challenges associated are discussed with a focus on the geotechnical aspects of the problem. The paper also presents an assessment of cost and environmental benefits tied with three application scenarios in pavement construction. It is understood for some applications that at this stage, MICP may not be a cost-effective or even environmentally friendly solution, however, following the latest developments, MICP has the potential to become one.