First published 9/29/2026, 3:36:15 PM · Last updated 9/29/2026, 3:36:15 PM

Mix design optimization and performance prediction of multi-source solid waste-based backfill materials using response surface methodology and BP neural network

A journal article published 2026-03-01 by Elsevier BV reports research on optimizing mix design and predicting performance of backfill materials made from multiple solid-waste sources. The supplied excerpt includes no methodological details, results, or geographic information.

Categories: science-and-space, technology, environment-and-climate

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 3/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 1/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 2/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 2/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 7/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 1/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The study addresses optimization of waste-derived backfill materials, which is relevant to materials science and waste-management research.

Sources

Mix design optimization and performance prediction of multi-source solid waste-based backfill materials using response surface methodology and BP neural network

Report an issue