\n 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.

Recycling and reuse of solid wastes; a hub for ecofriendly, ecoefficient and sustainable soil, concrete, wastewater and pavement reengineering

Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg–Muarquardt Backpropagation (LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil’s strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (wL), (plastic limit (wP), plasticity index (IP), optimum moisture content (wOMC), clay activity (AC), and (maximum dry density (δmax). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models’ accuracy.

Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil

Strength improvement of expansive soil by utilizing waste glass powder

Scheffe optimization of swelling, California bearing ratio, compressive strength, and durability potentials of quarry dust stabilized soft clay soil

Rheology of mechanical properties of soft soil and stabilization protocols in the developing countries-Nigeria

The effect of ordinary Portland cement, OPC+Quarry Dust, QD on the adsorbed moisture, diffused double layer (DDL), dielectric constant, density and repulsion potential (RP) of treated lateritic soil was investigated through laboratory tests. The preliminary tests showed that the natural soil was an A-2-7 soil, according to the AASHTO classification system, highly plastic soil and high swelling potentials. The soil was treated with a fixed 5% OPC and varying proportions of QD at 5, 10, 15, 20, 25, 30, 35, 40, 45 and 50% by weight of the dry soil. The stabilization results showed that the compaction properties improved consistently, with the addition of the QD. Also, the addition of the QD reduced the adsorbed moisture and consequently reduced the double diffused layer and the repulsion potential, which constitute the properties investigated within the adsorbed complex in the stabilization operation. These observations brought about the cation exchange reaction between the metallic ions that were attracted to the adsorption complex, resulting to densification, flocculation from the natural state of dispersion of particles and strength gain in the stabilization procedure. Results also showed that the repulsion potential increased in magnitude with the distance between the reactive particles and the clay surface and reduced with increased proportions of QD. The dielectric constant also reduced considerably with adsorbed moisture, which indicated that the dielectric was affected by the moisture and the ions released within the adsorbed complex.

Bui Van Duc

Papers

Adsorbed complex and laboratory geotechnics of Quarry Dust (QD) stabilized lateritic soils