PET waste management in Pakistan through use of PET shreds as additive in backfill soil.

The management of waste plastic bottles is one of the major environmental challenges in the world. Plastic bottles are composed of polyethylene terephthalate (PET), which is non-biodegradable, resulting in environmental problems. Various studies have been carried out on the use of waste PET bottles in the form of custom-made strips as a stabilizer. However, no significant research has been carried out on the use of waste PET bottle shreds already available in the market. These shreds do not require any special technology or arrangement for bulk production. In this study, the shear strength of low plastic silty clay was improved using locally available PET shreds, and their prospective application in the backfill soil was investigated. Standard Proctor tests and direct shear tests were conducted on soil stabilized with three different sizes of plastic shreds (2 mm, 6 mm, and 10 mm) in four different percentages (1%, 3%, 5%, and 10%). Findings revealed that adding PET shreds in 1% content improves the shear strength characteristics. However, the shear strength parameters decrease with further increase in PET shred content. Therefore, PET shreds in 1% content can be added in backfill soil to improve its shear strength. Pakistan needs to construct 0.77 million housing units annually to keep up with its population growth. The statistics of seven major cities of Pakistan show that the PET waste management issue of Pakistan can be resolved by using PET shreds as a backfill additive in only 32% of the new houses required to be constructed.

A robust prediction model for evaluation of plastic limit based on sieve # 200 passing material using gene expression programming.

This study aims to propose a novel and high-accuracy prediction model of plastic limit (PL) based on soil particles passing through sieve # 200 (0.075 mm) using gene expression programming (GEP). PL is used for the classification of fine-grained soils which are particles passing from sieve # 200. However, it is conventionally evaluated using sieve # 40 passing material. According to literature, PL should be determined using sieve # 200 passing material. Although, PL200 is considered the accurate representation of plasticity of soil, its' determination in laboratory is time consuming and difficult task. Additionally, it is influenced by clay and silt content along with sand particles. Thus, artificial intelligence-based techniques are considered viable solution to propose the prediction model which can incorporate multiple influencing parameters. In this regard, the laboratory experimental data was utilized to develop prediction model for PL200 using gene expression programming considering sand, clay, silt and PL using sieve 40 material (PL40) as input parameters. The prediction model was validated through multiple statistical checks such as correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and relatively squared error (RSE). The sensitivity and parametric studies were also performed to further justify the accuracy and reliability of the proposed model. The results show that the model meets all of the criteria and can be used in the field.