Evidence map›Paper›PMID 42011551›Full record

ReviewWaste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA2026

Integrating automation into construction waste quantification: A review of digital innovations for sustainable resource management.

Moinul Haq, Osama Mohsen, Mohamed T Elnabwy, Pablo Martinez

Abstract readReview
In one paragraph

Review in Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Moinul HaqInterdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.ORCID 0000-0001-9059-0697
Osama MohsenInterdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.ORCID 0000-0002-3992-9357
Mohamed T ElnabwySchool of Architecture and Built Environment, Northumbria University, Newcastle upon Tyne, UK.ORCID 0000-0003-2461-0148
Pablo MartinezSchool of Architecture and Built Environment, Northumbria University, Newcastle upon Tyne, UK.ORCID 0000-0003-3397-9617

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The construction sector is a major generator of solid waste, with construction and demolition waste (CDW) posing significant challenges to sustainable resource management. Accurate quantification is vital for achieving circular economy and waste reduction goals; however, traditional estimation methods remain manual, fragmented and inconsistent. This state-of-the-art review synthesizes developments in automation and digital technologies that are transforming CDW quantification and management. A total of 125 peer-reviewed articles published between 1993 and 2025 were systematically analysed to identify trends, methods and emerging tools. Advances across artificial intelligence, computer vision, building information modelling and the Internet of Things are categorized, focusing on automated waste recognition, volumetric estimation and real-time monitoring. Persistent challenges, including limited dataset diversity, model generalization, interoperability and implementation cost, are critically examined. By linking technological innovation with practical waste management, this review highlights how automation can enhance efficiency, traceability and sustainability in the construction industry's transition towards circular practices.

Indexed as

AutomationConstruction IndustrySolid WasteWaste ManagementArtificial IntelligenceConservation of Natural ResourcesSolid Wasteartificial intelligencecircular economycomputer visionconstruction and demolition wastesustainable constructionwaste quantification

Identifiers

PMID42011551
PMCPMC13530366

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.