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.

Enzyme-induced carbonate precipitation (EICP) is a relatively new bio-cementation technique for ground improvement. In EICP, calcium carbonate (${\mathrm{CaCO}}_3$) precipitation occurs via urea hydrolysis catalysed by the urease enzyme sourced from plants. EICP offers significant potential for innovative 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 ${\mathrm{CaCO}}_3$ precipitation via the ureolytic chemical reaction is vital for optimising the process. For instance, in a typical EICP process, the rate and quantity of ${\mathrm{CaCO}}_3$ precipitation can depend significantly 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 ${\mathrm{CaCO}}_3$ precipitation in EICP. The paper also presents the assessment and estimation of kinetic parameters, such as the maximal reaction velocity (${\mathrm{V}}_{\max }$) and the Michaelis constant (${\mathrm{K}}_{\mathrm{m}}$), 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 ${\mathrm{CaCO}}_3$ precipitation in EICP.

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.

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 (E_NS) and the index of agreement (d). The GPR, BPNN and PCR models, respectively, gave an E_NS 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.