Development Of Recycled Lightweight Concrete With Intelligent Environmental Monitoring System For Temporary Housing In Vulnerable Regions

Vasquez M.J.C.; Tirado C.E.M. · 2025 10th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2025 (2025) · DOI: 10.1109/seeda-cecnsm68644.2025.11330124

Resumen

This study proposes the development and evaluation of a structural-grade lightweight concrete incorporating recycled aggregates derived from construction and demolition waste (C&DW), integrated with an embedded environmental monitoring system and an artificial intelligence (AI) model for real-time data analysis. The objective is to provide a sustainable and intelligent construction solution for emergency and transitional housing in climatically extreme regions of Peru. Four concrete mixes were designed, including one control and three experimental formulations with 25%, 50%, and 75% recycled aggregate replacement by volume. All mixes were tested for fresh and hardened density, as well as compressive strength at 7 and 28 days. Results confirmed that even at the highest level of replacement, the concrete achieved compressive strengths above 20 MPa and densities within the structural lightweight range (<1800 kg/m3) validating the feasibility of using C&DW in structural applications without compromising mechanical performance. A cyber-physical system composed of temperature, humidity, and CO2 sensors was embedded into the concrete using 3D-printed protective capsules. Data were transmitted wirelessly via MQTT protocol to a local server, stored in InfluxDB, and visualized using Grafana dashboards. A feedforward artificial neural network trained on this sensor data demonstrated over 90% accuracy in detecting environmental anomalies, supported by dropout and early stopping techniques to prevent overfitting. The integration of recycled materials, embedded sensor networks, and AI-based analytics presents a novel, low-cost, and scalable approach to smart construction. This framework offers strong potential for implementation in post-disaster or rural settings, where resilient, modular, and intelligent housing infrastructure is urgently needed.

Instituciones: Universidad Continental