Sustainable Materials and Intelligent Monitoring for Capillary Barrier Systems in Reinforced Soil Structures: A PRISMA-Based Review

Abstract

Capillary barrier systems (CBS) improve geotechnical structures by controlling water infiltration under unsaturated conditions. By exploiting hydraulic contrasts between fine and coarse layers, CBS effectively diverts water flow and reduces excess pore-water pressure. When integrated with reinforced soil structures (RSS), CBS enhances stability by maintaining matric suction and mitigating rainfall-induced softening, thereby offering a climate- resilient and resource-efficient alternative to traditional drainage methods. Driven by sustainability and circular economy objectives, the adoption of recycled and marginal geomaterials-such as reclaimed asphalt pavement, recycled concrete aggregate, and local soils–has increased. In addition, recent advances in measurement technologies and data analytics enable intelligent monitoring of CBS-RSS systems, representing a significant advancement in geotechnical engineering innovation. However, the specific contributions and limitations of sustainable materials and data-driven monitoring approaches for CBS-RSS remain unclear, hindering their wider adoption in practice. Therefore, this study employed a PRISMA-compliant systematic review to identify, screen and synthesize 82 studies on CBS in RSS, focusing on two thematic clusters: sustainable and alternative materials, and monitoring technologies and data-driven/intelligent approaches. The study shows that recycled materials such as Reclaimed asphalt pavement, recycled concrete aggregate and steel slag can provide the hydraulic contrast required for CBS layers, but that multi-year field validation, leachate characterization and standardized SWCC testing protocols for recycled geomaterials are largely missing. In addition, the review exhibit that instrumented CBS-RSS systems equipped with tensiometers, moisture sensors, piezometers and, more recently, sensor-embedded geosynthetics and wireless or fiber-optic networks can deliver rich datasets, yet these data are rarely assimilated into digital-twin or physics-informed machine-learning frameworks for real-time performance assessment and design optimization. The review connects CBS technology with sustainability and intelligent monitoring, establishing CBS-RSS as a climate-resilient, resource-efficient, and intelligent alternative to conventional retaining systems, and delineating key research priorities for broader implementation.