The main purpose of this paper is to study the feasibility of using wood bottom ash to partially replace natural fine aggregate or crushed gneiss sand in the manufacturing of mortars. The experiment uses wood ash as fine aggregates, which passes through 5 mm sieve, in proportions of 5%, 10%, 15%, 20% and 25% by weight to replace partially river sand and crushed gneiss, and the both sand of the same size as the aggregate respectively. Experimental results show that density of mortar and the compressive strength of mortar decrease globally with the increase in wood ash content. At 56 days, and for all replacements with wood ash, compressive strengths values of mortar obtained with the mixture of wood ash and river sand is greater than 20 MPa, which is not the case for mortar made with crushed gneiss and wood ash. Moreover, for 5% of replacement with wood ash, compressive strengths of mortar obtained with the mixture of wood ash and river sand and the mixture of wood ash and crushed gneiss are respectively 37 MPa and 32 MPa at 56 days. These values satisfied the strength requirements. Hence, 5% replacement of crushed gneiss with wood ash is suggested and could be benefit for mortar. In addition, the replacement of sand by wood ash is preferable with river sand which contains fewer fines than crushed gneiss. The compressive strength of mortar with 25% wood ash + river sand could be suitable.

The objective of this study is to model the evolution of the values of the modulus of elasticity E as a function of the bearing capacity index ICBR of Banka lateritic gravel, with the aim of reducing the cost and time of laboratory tests for the determination of the modulus of elasticity and the CBR index. Geotechnical identification tests were carried out on 18 laterite samples taken from the Banfeko, Bakoye and Ketcho sites in the Banka locality. ICBR values ranged from 31 to 51.92 and E-modulus values ranged from 80 MPa to 165 MPa. The resulting model is polynomial for all sites of the form: E = a* ICBR 5 + b* ICBR 4 + c* ICBR 3 + d* ICBR 2 + e* ICBR +f This model has the highest degree of interdependence of value 1.

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