The urban agglomeration in India has been expanding in recent years. This has resulted in a significant surge in new projects within the building sector. The renovation and construction of different types of infrastructure result in millions of tons of construction and demolition waste (CDW) all around the country. Inadequate sorting and management of these wastes would have a detrimental effect on the environmental system. The large bulk of CDW produced is disposed of in landfills. At the same time, building new facilities will need more funding. Natural resources are becoming depleted, which means that extraction costs for high-quality materials will increase, and an alternate supply of materials for road building may be found that is both economically feasible and environmentally friendly. Hence recycling the CDW can be utilized in construction processes. The effectiveness of stabilizing CDW as a base layer substitute for natural aggregate in flexible pavement is investigated in this study. Based on these lab experiments, it is determined that a base layer of CDW may be used in the future. Further, the prepared CDW-based pavement characteristics are studied using FE-SEM, XRD, and FTIR. The chemical properties of waste material show its suitability in stabilization. CDWs stabilized with 3, 4, 5, and 6% of RBI Grade 81 are considered for the proctor compaction test. There is an increase in OMC and MDD as the RBI Grade 81 content increases. All the mixes are considered for unconfined compressive strength test and durability test of wetting and drying. Mix with 5% and 6% RBI Grade 81 content to attain the specified strength range of 4.75–7 MPa in 7 days of curing. An Artificial Neural Network (ANN) has been built to predict the compressive strength at 7 and 28 days using different input parameters. The ANN model predictions and experimental results showed strong correlations.

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Application of CDW Materials to Enhance the Compressive and Microstructural Characteristics of Flexible Pavement Structure Using Artificial Neural Network (ANN)

  • Krishnaraj Khatri,
  • Shailendra Kumar,
  • Chandresh Solanki

摘要

The urban agglomeration in India has been expanding in recent years. This has resulted in a significant surge in new projects within the building sector. The renovation and construction of different types of infrastructure result in millions of tons of construction and demolition waste (CDW) all around the country. Inadequate sorting and management of these wastes would have a detrimental effect on the environmental system. The large bulk of CDW produced is disposed of in landfills. At the same time, building new facilities will need more funding. Natural resources are becoming depleted, which means that extraction costs for high-quality materials will increase, and an alternate supply of materials for road building may be found that is both economically feasible and environmentally friendly. Hence recycling the CDW can be utilized in construction processes. The effectiveness of stabilizing CDW as a base layer substitute for natural aggregate in flexible pavement is investigated in this study. Based on these lab experiments, it is determined that a base layer of CDW may be used in the future. Further, the prepared CDW-based pavement characteristics are studied using FE-SEM, XRD, and FTIR. The chemical properties of waste material show its suitability in stabilization. CDWs stabilized with 3, 4, 5, and 6% of RBI Grade 81 are considered for the proctor compaction test. There is an increase in OMC and MDD as the RBI Grade 81 content increases. All the mixes are considered for unconfined compressive strength test and durability test of wetting and drying. Mix with 5% and 6% RBI Grade 81 content to attain the specified strength range of 4.75–7 MPa in 7 days of curing. An Artificial Neural Network (ANN) has been built to predict the compressive strength at 7 and 28 days using different input parameters. The ANN model predictions and experimental results showed strong correlations.