Ecofriendly, ecoefficient and sustainable civil engineering work has been research with emphasis on adapting the byproducts of solid waste recycling and reuse to achieving infrastructural activities with low or zero carbon emission. The direction combustion model, the solid waste incinerator caustic soda oxides of carbon entrapment model (SWI-NaOH-OCEM) developed by this research has achieved a zero carbon release. This research adopted the literature search method to put together research results of previous works relevant to the aim of this present work. It has been shown that CO and CO2 emissions can be contained during the derivation of alternative or supplementary cementing materials used in the replacement of ordinary Portland cement in civil engineering works. In the overall assessment of the present review work has left the environment free of the hazards of CO and CO2 emissions. It was shown that these supplementary cementing materials derived from solid wastes improve the engineering properties of treated soft clay and expansive soils, concrete, and asphalt. Bio-peels, another form solid waste has been established as a good detoxificant used in treating wastewater. It has been shown that solid waste recycling and reuse is a hub to achieving ecofriendly, ecoefficient and sustainable infrastructural development on the global scale.

An experimental study was carried out on an expansive black cotton soil (BCS) treated with lime and bamboo ash (BA) to determine its suitability as subgrade material. Preliminary tests conducted on the natural BCS indicated that the soil cannot serve as subgrade in the design of flexible pavement as the values obtained failed to meet the recommendations of the Nigeria general specification. Tests were carried out to determine the compaction and strength characteristics of the stabilised soil using an optimal 4% lime content selected based on plasticity index value, with various percentages of BA (4%, 8%, 12%, 16% and 20%). After stabilisation, an optimal value of 4% lime and 12% BA, added by weight of the air-dried soil showed that the compaction and strength characteristics of the soil improved significantly due to chemical actions experienced between the admixtures and the soil. Furthermore, whilst the resilient modulus (M_R) of the natural soil decreased with increasing deviatoric and bulk stresses, the M_R of the stabilised soil improved significantly with increase in deviatoric and bulk stresses. The scanning electron microscope analysis of the specimen admixed with the optimal value and subjected to 28 days curing clearly showed that cementitious compounds were formed.

The adoption of mechanistic-empirical approach to pavement design requires the use of resilient modulus of subgrade soils as a crucial input. The determination of in the laboratory is inexpedient due to the nature of the existing test protocols. This prompted the use of estimated values, which inadvertently has gained popularity lately. However, the accuracy of estimated values is questionable due to spatial variability of soil properties. This necessitated the aggressive search for robust and thorough approaches for predictive modelling of the . In the present study, a systematic approach was adopted for the descriptive analysis and estimation of . from routine soil properties using data from Long-Term Pavement Performance (LTPP) and considering the spatial variability of the soil properties. Descriptive analysis was executed using non-parametric correlation and principal component analysis (PCA), while the estimation was done using three machine learning methods which include gradient boosting regression (GBR), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Based on the PCA, four factors which explained a total of 77.5% variance in the data had significant influence on the . These include the effect of moisture-induced changes on the soil consistency limits and physical condition, effect of the soil clay content, effect of the soil gradation and effect of the soil stress state. Various factors of the machine learning methods such as the learning rate, number of clusters and number of hidden layers had a significant effect on the prediction accuracy. The three machine learning methods were satisfactory for the prediction based on R² values which were generally above 0.9. Also, when considering spatial variability of routine soil properties, the GBR and ANFIS have a comparative advantage over the ANN, since they exhibited a high stability in the prediction for both the training and testing dataset.