Investigation of the possible valorization of dredged sediments in compressed earth blocks

Abstract

The construction sector is currently facing an environmental challenge and construction materials with low CO₂ emissions are needed. In view of this ecological transition, earth constructions are an interesting solution given their low environmental impact and their hygrothermal properties. On the other hand, the ports must be regularly maintained by dredging operations in order to provide the necessary water depth for navigation. This induces huge amounts of sediments that should be valorized as they cannot be discharged in the sea with the evolution of dredging practices laws. This study investigates the potential use of “Arcachon Bay” dredged sediments in South West France in Compressed Earth Blocks (CEB). Several sediment samples were collected from eight different locations along the bay to take into consideration their variability. The characterization of the sediments has been conducted in a cross way by laboratory and field tests to provide a feedback on the limitations and the complementarity of the two approaches and improve the current practices. The correlation between geotechnical and field tests has been also analysed. The field tests allow the characterization of sediments through sensory and cohesion tests such as appearance, smell, plasticity, clay content, cigar, pellet, shine, hand washing and adhesion. The conducted laboratory tests were wet sieving granulometry, sedimentation, Atterberg limits, Organic Matter (OM) content and Methylene Blue Value (MBV). The GTR classification has been also determined and the results showed that most of the sediments are silty clay of class A2 or A3. Based on those data, the compliance of these sediments with the CEB manufacturing recommendations is analysed. Several mix optimization techniques have been proposed to improve the mechanical properties such as adapting the granulometry by adding sand or opting for a chemical stabilization. In addition, a machine learning model based on a dataset collected from the literature has been developed to predict the compressive strength of CEB and optimize the mix and fabrication conditions.