Sustainable Hybrid Modification of Asphalt Binder Using Styrene-Butadiene-Styrene and Waste Polyvinyl Chloride for Enhanced High-Temperature Performance

2026-04-23T11:10:05+00:00

Standard asphalt binders are thermally unstable; thus, modification is required to prevent rutting at high pavement temperatures and under heavy truck axle loads. Using a hybrid styrene-butadiene-styrene (SBS) and waste polyvinyl chloride (PVC) technology, a standard asphalt binder (60/70 penetration grade) was sustainably modified to improve high-temperature performance and rheological stability. Virgin control and nine hybrid mixes containing SBS (2%, 3%, and 4%) and waste PVC (1%, 3%, and 5%) by weight were tested. This hybrid change stiffened the matrix, reducing penetration and increasing softening point, according to conventional tests. Rotational viscosity rose with SBS and waste PVC concentrations, improving flow resistance at mixing and compaction temperatures. PVC concentrations elevated rolling thin film oven (RTFO) mass loss and lowered flash point due to waste polymer thermal instability. PVC’s plasticity partially disrupts the SBS’s continuous elastic network, decreasing elastic recovery as waste PVC concentration increased. Using the dynamic shear rheometer, a transition from viscous to elastic behavior was observed, with greater G* and lower δ values. The original and RTFO-aged binders greatly improved in rutting parameter (G*/sinδ), indicating increased ageing resistance. In the base performance grade (PG) 64-16 binder, hybrid SBS/PVC raises the high-temperature PG by two to six levels. These findings enhance asphalt binder performance in extreme temperatures and weather, enabling resilient pavement systems and more sustainable, durable, and cost-effective road infrastructure.

Performance Enhancement of Low-Density Parity-Check Decoder Using Neural Network Optimized Parameters

2026-04-12T23:36:01+00:00

Low-density parity-check (LDPC) codes are of prime importance in achieving near-Shannon capacity in current communication systems. However, the optimal decoding process for LDPC codes is computationally intensive. This paper presents a neural normalized min-sum (NNMS) decoding network that improves error correction capabilities with minimal computational overhead. A weight-sharing approach is adopted in the NNMS model, in which the correction factors (α, β) are shared across nodes within a single layer. This approach decreases the total number of parameters in the model compared to traditional neural decoders, making it easier for hardware implementation. The NNMS model is tested using a (576, 432) LDPC code for an additive white Gaussian noise channel with binary phase shift keying modulation. Simulation results show that the NNMS model outperforms traditional normalized min-sum (NMS) decoders. At a signal-to-noise ratio of 5 dB, the NNMS model has a bit-error rate (BER) of 1.0 × 10−7, whereas traditional NMS models have a less steep slope. In particular, the NNMS model has a coding gain of 1.25 dB compared to the traditional NMS model at a BER threshold of 1.0 × 10−3. This shows that the NNMS model is an efficient solution for high-performance, real-time digital communication systems.

UHD Journal of Science and Technology

Sustainable Hybrid Modification of Asphalt Binder Using Styrene-Butadiene-Styrene and Waste Polyvinyl Chloride for Enhanced High-Temperature Performance

Performance Enhancement of Low-Density Parity-Check Decoder Using Neural Network Optimized Parameters