Identifying Root Causes and Sustainable Solutions for Reducing Construction Waste Using Social Network Analysis

Abstract

The construction industry is a major contributor to environmental degradation, primarily due to the substantial volumes of construction waste (CW) generated on-site. As sustainability becomes a global imperative aligned with the UN 2030 Agenda, identifying and mitigating the root causes of CW is essential. This study adopts a cross-disciplinary approach to explore the drivers of CW and support more effective, sustainable waste reduction strategies. A systematic literature review was conducted to extract 25 key CW source factors from academic publications. These were analyzed using Social Network Analysis (SNA) to reveal their structural relationships and relative influence. The results indicate that the lack of structured on-site waste management planning, accumulation of residual materials, and insufficient worker training are among the most influential CW drivers. Comparative analysis with industry data highlights theoretical–practical gaps and the need for improved alignment between research insights and site implementation. This paper recommends the adoption of tiered waste management protocols as part of contractual documentation, integrating Building Information Modeling (BIM)-based residual material traceability systems, and increasing attention to workforce training programs focused on material handling efficiency. Future research should extend SNA frameworks to sector-specific waste patterns (e.g., pavement or demolition projects) and explore the intersection between digital technologies and circular economy practices. The study contributes to enhancing waste governance, promoting resource efficiency, and advancing circularity in the built environment by offering data-driven prioritization of CW sources and actionable mitigation strategies.

1. Introduction

The increase in waste production has become a global phenomenon that must be handled properly. Waste is any consumption of resources that adds no value in return, such as the production of unneeded items or mistakes that require rectification [1]. The percentages of waste generation differ from one sector to another, whereas the construction industry is the most significant resource consumer and has the highest waste production rates [2,3]. Recent studies indicate that globally, more than 10 billion tons of construction and demolition (C&D) waste are generated annually, with significant contributions from China, India, the USA, and the European Union (EU) [3]. Developed countries, such as the USA and UK, contribute a substantial portion of this waste, up to 35% ending in landfills [4]. This highlights the pressing need for sustainable waste management practices, particularly in regions with high waste generation.

Various researchers have attributed construction waste (CW) to material usage or excess quantities in the literature. Liu et al. [5] define CW as the materials transported outside or used on the site for purposes other than their intended purposes due to certain damage, excess quantities, non-compliance with the design or specifications, or even materials generated as a by-product of construction activities. Ramaswamy and Kalidindi [6] considered CW from a lean paradigm as using non-added value resources to deliver the final product. According to Lam et al. [7], CW is the waste generated from construction operations of new buildings, infrastructures, excessive materials, retrofitting, renovation, and demolition of old structures. At the same time, Akhund et al. [8] define CW as the difference between the quantities of the estimated materials and the actual quantities used in a construction project. Viana et al. [9] see CW as material that is no longer wanted or has no residual value.

These definitions highlight the critical issues of resource wastage in the industry. However, recent advancements in circular economy (CE) principles have redefined how construction waste is viewed and managed. These principles focus on minimizing waste through reuse and recycling at every stage of the construction lifecycle. Integrating Building Information Modeling (BIM) has enabled more efficient material usage, allowing for precise quantification and strategies to reduce waste during project planning and execution [10]. CW arises at various project stages, from inception and design to construction, operation, and demolition [11]. The life of a construction facility is a one-way cycle where different materials are essential for construction or renovation. After its lifecycle, the structure is demolished and turned into debris that is no longer wanted or has no residual value [9]. This waste is then incinerated or dumped into landfills (Figure 1).

Figure 1. Lifecycle of building materials and associated waste types.

Construction waste comprises diverse materials originating from various construction, demolition, and renovation activities. It typically includes concrete, masonry, wood, metals, asphalt, glass, plastics, and other mixed debris, each presenting unique challenges and recycling potentials [5,9]. Among these, concrete waste is one of the largest fractions, often generated as fines or coarse aggregates during demolition or site clearance [12]. Recent advancements in recycling concrete fines have demonstrated their potential not only as inert filler materials but also as functional substitutes for traditional binders and aggregates in alkali-activated mortars, thus enabling a circular reuse pathway that enhances both environmental sustainability and material performance [13]. This work exemplifies the valorization of concrete waste fines, moving beyond landfill disposal towards value-added material recovery [13].

Similarly, asphalt pavement waste, representing a significant proportion of road rehabilitation debris, exhibits high recyclability rates (up to 95%) when appropriately processed and reused in pavement reconstruction [14,15]. As such, recycling asphalt pavement (RAP) has become essential in achieving sustainable construction and reducing environmental impact [15]. However, the integrity and durability of recycled construction materials remain a crucial concern. Cutting-edge studies using in situ 4D computed tomography (CT) have revealed microcrack evolution patterns in carbonated fiber-reinforced recycled aggregate concrete, providing insights into the material’s long-term mechanical behavior and durability under real-time environmental conditions [16]. Such research contributes critical understanding necessary for optimizing recycling processes and improving the lifecycle performance of recycled construction materials. Overall, these components, concrete fines, and asphalt pavement waste illustrate the complexity of construction waste composition and highlight the importance of integrating material science innovations with waste management strategies to enhance sustainability in the construction sector.

According to Napier [17], the production of CW creates schedule delays and cost escalations while increasing the environmental burden of the project and impacting its sustainability performance. Hence, there have been financial, environmental, political, and social pressures to maintain a sustainable construction industry, which has led to an emphasis on conducting more effective research on CW management. Researchers in the construction industry need to study and develop efficient tools and approaches to declare the CW sources, quantify the predicted quantities, and minimize their escalating production percentages.

This study started with a literature review of the publications within the last 30 years to capture the research measurements of waste root sources in the construction community. The work indicates the main causal factors of CW and how to minimize their effects. It targets determining the liability of various design and construction activities for CW production, collecting the most influential source factors from the literature, and developing a sustainable construction environment roadmap. This research is essential for broader initiatives aiming to conceptualize CW management theories.

2. The Problem of CW

Any construction project is a chain of connected activities where any defect in one activity would adversely affect the successive ones, resulting in redundant and defective work [18]. Therefore, demolishing and reconstructing the inadequate element may require additional cost, time, and material waste. The following subsections discuss CW’s various classifications, types, measurements, and disposal approaches.

2.1. CW Classifications and Types

The construction waste literature mainly focuses on waste materials from construction, rehabilitation, or demolition projects. Jaillon et al. [19] divide CW materials into inert materials, such as soil and slurry, and non-inert materials, such as metal, wood, bricks, and packaging. Meanwhile, Formoso et al. [20] classifies CW into direct and indirect waste. Direct waste is the type that indicates completely damaged materials, and indirect waste refers to inaccurate work. In 2018, the US Environmental Protection Agency (EPA) classified various CW materials into nine major categories: concrete, steel, wood, brick, gypsum, wallboard and plaster, asphalt, and asphalt shingles [21]. Nagapan et al. [22] classifies CW into two clusters: physical and non-physical waste. Physical CW is a mixture of materials generated from various construction-related activities such as construction, renovation, and demolition [23]. It includes concrete debris, brick blocks, tiles, reinforcement, plastic materials, paper, wood, gravel, and soil [24]. The non-physical CW represents non-value-added work, also known as intangible or indirect waste. From a lean construction perspective, waste is beyond the excess amount of material on site that is not needed. It could be generalized to include eight aspects that can affect project progress [25], including transportation, inventory, motion, waiting, over-production, over-processing, defects, and skills.

The eight aspects conceptualize waste in construction projects, starting from unnecessary transportation and moving material, equipment, and on-site workers, leading to time and effort waste. The other two aspects are the early delivery of material that consumes the available inventory spaces or the late delivery that leads to more waiting times. They consider the defect waste resulting from low and underutilized skills to be something that can also lead to over-production and an over-processing workflow.

2.2. CW Measurements

Prediction of CW quantities from a project is paramount for waste management. Multiple efforts were made to quantify the generated material waste from construction activities. Lau et al. and Foo et al. [26,27] build a model that quantifies five types of physical waste from construction projects, including concrete, timber, steel, brick, and packaging waste materials. This quantification considered the volume of stockpiled waste based on a rectangular prism or pyramidal shape. They concluded that timber waste has the highest value among the five waste types. Similarly, quantifying pavement waste is crucial for effective waste management in road construction projects, particularly for asphalt and concrete. RAP and recycled concrete aggregate (RCA) generation rates can be calculated based on project-specific factors, such as the pavement surface area and thickness. One standard method for estimating construction and pavement waste is applying generation rates, typically expressed in kilograms per square meter (kg/m²) for concrete and asphalt [28]. According to Hao et al. [29], Equation (1) is most applied to quantify CW throughout the construction phase:

P = A × i

(1)

where P is CW (kg), A is the construction area (m²), and i is the CW generation rate (CWGR) (kg/m²).

Bakshan et al. [30] assessed the CWGR value to be 38 to 43 kg/m². Kofoworola and Gheewala [31] reported that the CWGR equals 21.38 kg/m² for residential buildings and 18.99 kg/m² for non-residential buildings. Li et al. [32] evaluated the CWGR by studying a new residential building in China at a 40.7 kg/m² waste generation rate. This variation in CWGR is due to the difference between each project region, building type, and construction method. In recent years, Digital technologies, including BIM and IoT-based systems, have become increasingly significant in minimizing CW. These technologies enhance material tracking, management, and real-time monitoring, allowing construction teams to proactively manage resources and reduce waste on-site [10,33]. By using BIM, for example, precise material quantities can be estimated during the design phase, reducing the risk of over-ordering or under-utilization. Moreover, adopting CE in construction transforms how materials are handled throughout the project lifecycle [4], reducing the overall demand for virgin materials. This shift supports more sustainable and resource-efficient practices across the industry.

2.3. Reuse and Recycling Practices in CW Management

Material reuse and recycling are increasingly acknowledged as central to achieving sustainable CW mitigation [34]. Reuse refers to the direct redeployment of materials without undergoing substantial processing, such as salvaging bricks, timber, steel frames, and fittings for future use [35,36], while recycling entails transforming discarded materials into usable raw inputs for new applications [28,37]. This includes crushing demolished concrete for RCA or repurposing RAP in road construction projects [28,37].

A growing body of research highlights the environmental, economic, and logistical benefits of implementing reuse and recycling practices in construction. Some scholars have proposed models for enhancing the feasibility of on-site reuse to lower transportation-related emissions and decrease disposal volumes [38]. Others have evaluated the performance and structural adequacy of incorporating recycled materials, such as RCA and recycled steel, into structural and non-structural construction applications [28,37]. These studies confirm that recycling conserves natural resources and reduces lifecycle costs and carbon footprints associated with material procurement.

In road infrastructure projects, advancements in hot- and cold-recycling methods have enabled high-percentage RAP integration in new asphalt layers, thus reducing dependency on virgin bitumen [28,39]. Similarly, RCA is increasingly used in base layers, backfill materials, and even in new concrete formulations under specific standards [37]. Lifecycle assessment (LCA) studies further validate the environmental viability of these practices [28,37]. Nonetheless, institutional, technical, and regulatory limitations often constrain reuse and recycling strategies [40]. Barriers such as unclear policy frameworks, variability in recycled material quality, and limited stakeholder awareness pose significant challenges. Moreover, many regions lack standardized specifications that mandate or incentivize the use of recycled content in public and private construction projects [40].

2.4. CW Disposal

CW management practices would be the main guidelines for organizing the disposal process according to the permissions and standards of each region [30]. These practices stand to reason that elimination, minimization, and reuse of CW are essential. It is expensive and complicated and requires more coordinated, creative, persistent, and knowledgeable actions from business, professional, and government [17].

In some cases, the massive accumulated amounts of CW drive construction contractors to some inappropriate or illegal practices such as burying or disposing of the waste in abandoned areas, dumping in waterways, mixing CW with domestic wastes, or burning [34]. As a result, reducing and eliminating CW is crucial as an environmental solution to address illegal practices. Applying various CW minimization practices can significantly reduce accumulated waste at the source, targeting waste before it enters the waste stream [5].

On the other hand, pavement waste is a significant part of construction and demolition waste. Improper waste disposal can lead to environmental harm, as asphalt may release harmful chemicals into the soil and groundwater. However, advances in recycling, such as hot and cold asphalt recycling, allow for RAP reuse in new road projects, reducing the need for virgin materials [28]. Similarly, RCA, sourced from demolished concrete, can be repurposed for road base materials and new concrete, helping to minimize landfill waste [41]. These recycling techniques reduce environmental impacts and lessen the financial costs, aligning with sustainability goals in construction.

2.5. Integration of Digital Tools and Policy Frameworks in CW Management

Recent CW research identifies BIM as an increasingly important tool for managing construction and demolition waste because it enables automated quantity take-offs, supports demolition planning and clash detection, and facilitates the creation of reusable material databases [5,42]. As noted in [43], there is a growing number of BIM-based plugins and workflows that target early-stage waste estimation and material-reuse decision making, while noting persistent gaps in interoperability and lifecycle coverage. Empirical implementations have demonstrated that BIM-driven algorithms can automate estimation for specific waste streams (for example, concrete and drywall), thereby improving estimation accuracy and supporting on-site waste-reduction strategies [44]. More recent work proposes coupling BIM with sensing and tracking technologies, such as IoT devices, RFID, LiDAR, and drone imagery, to enable near-real-time material traceability during demolition and construction operations, which in turn facilitates selective demolition, on-site segregation, and circular reuse pathways [45,46]. While these digital innovations offer strong potential for operationalizing circular economy objectives (e.g., component banks and material passports), the literature also identifies practical challenges, including required levels of information, cross-platform interoperability, and implementation costs, which warrant empirical validation through pilot projects and supportive policy frameworks.

2.6. Knowledge Gap and Objectives

Although previous studies have investigated various CW categories, sources, and mitigation strategies, a systematic synthesis of CW source factors across the literature remains underdeveloped, particularly regarding how these sources interact and co-occur. Most existing reviews focus narrowly on technological solutions (e.g., BIM or recycling) or specific materials (e.g., concrete, asphalt), without establishing a broader, multi-factorial model of CW generation.

Furthermore, while the recycling of CW materials (e.g., RAP, RCA) is increasingly acknowledged, integrating recyclable material mechanisms within source-factor classification frameworks is still missing. There is also limited alignment between theoretical CW classifications and real-world contractor practices, especially in developing countries. To bridge this gap, this review offers an original contribution by the following:

• Systematically extracting and classifying 25 CW source factors from the literature (1995–2025) using PRISMA guidelines.
• Applying SNA to quantify CW sources’ influence and co-occurrence patterns is a novel technique rarely used in CW reviews.
• Identifying both consensus and blind spots across academia and industry practices.
• Recommending actionable guidelines based on integrated digital tools (e.g., BIM, RFID) and CE principles.

As such, the research questions are as follows:

• What are the most influential and frequently co-occurring factors contributing to CW in the literature?
• How do academic findings align with real-world site practices and expert perceptions?
• What digital and regulatory frameworks can address the most critical CW sources?

Through this structured, network-based review, the study aims to support future development of integrated CW management systems that emphasize recycling and prioritize waste prevention at the source.

3. Research Methodology

The current study follows an interdependent multistep framework to achieve its objectives (Figure 2). First, the authors conducted an extensive literature review to analyze retrieved construction waste studies covering the years 1995–2025 (Table 1). Then, the authors extracted the CW root source factors from the analyzed literature and performed SNA to identify the impact of each source. These factors represent the theoretical considerations of the CW problem in the body of knowledge in the literature. The SNA ranked the defined source factors according to their importance. After that, the defined factors were compared with those considered by construction practitioners, which were extracted from a previous CW study. From the SNA and the applied comparative analysis, the study highlights the factors that CW sources ignore in the research and actual practice domains. The following sections discuss all the study details, analysis, results, and recommendations.

Figure 2. Study framework.

Table 1. Review of some previous studies and their contribution.

Researchers	Country	The Research Area
[41]	China	The study classified China into three policy zones and proposed various recommendations as decision-based policies for CW management in China and similar countries on the road to a zero-waste society.
[47]	Australia	Defined 26 decision policies for CW management through qualitative and quantitative approaches by eliminating and/or minimizing CW generation.
[48]	Malaysia	Identified 12 CW minimization factors, indicating the most and least significant and practiced factors of waste minimization in Malaysia.
[27]	Malaysia	Discussed the identification and estimation of the waste accumulated from construction sites.
[49]	Malaysia	Presented the issues surrounding CW generation in Malaysia, such as illegal dumping and waste management strategies through disposal methods into landfills.
[50]	Malaysia	Reviewed the existing literature on CW and various initiatives implemented in Malaysia.
[19]	Hong Kong	Discussed some policies for waste reduction in Hong Kong, such as prefabrication in construction buildings. The study reported a 52% average waste reduction level by applying prefabrication.
[51]	Hong Kong	Investigated the effectiveness of CW management policies in Hong Kong by conducting some interviews and questionnaires.
[52]	Hong Kong	Examined various CW policies and their effects on the construction industry in Hong Kong using qualitative data from interviews and previous case studies.
[53]	India	Focused on the economic feasibility and cost savings from CW minimization policies in India.
[54]	India	Explored the motivators of individuals in the workforce for implementing CW management in India.
[55]	India	An overview of the Indian construction industry and some construction and demolition waste generation statistics were reported.
[5]	UK	Developed a BIM-based framework for minimizing CW in the design stage through the results of a literature review, questionnaire, and interview data with 100 top engineers in the UK.
[56]	Kuala Lumpur	Studied the main CW management challenges for contractors in Kuala Lumpur and provided some recommendations to face them.
[57]	Sri Lanka	The study focused on attitudes and perceptions of the workforce towards CW management applications.
[58]	Spanish	Proposed a BIM method to identify and estimate CW quantities during the design stage.
[59]	Bangladesh	Identified various CW-generating factors and developed the appropriate management strategies for the Bangladesh construction industry by interviewing construction professionals and rigorously reviewing the previous literature.
[60]	Chile	Applied the Delphi method to uncover that technical, regulatory, financial, and socio-environmental factors influence the adoption of sustainable CW practices in Chile.
[61]	Europe	Explored how European countries can enhance the management of construction and demolition waste by adopting CE strategies through improved policies, technologies, and industry practices.
[62]	Poland	Investigated key inefficiencies within construction projects in Poland and targeted recommendations grounded in BIM and Lean principles to address recurring issues such as schedule delays and coordination challenges through integrated digital and process-driven solutions.
[63]	Egypt	Quantified construction and demolition waste in Egypt across various project types, identifying timber, sand, and bricks/blocks as the most wasted materials, with infrastructure projects showing the highest waste and cost losses. It finds that stronger waste-reduction practices, awareness, and legislation are linked to lower waste amounts.
[64]	South Africa	Employed a quantitative approach to evaluate the effectiveness of existing construction and demolition waste management practices in South Africa and to identify areas for improvement to enhance sustainability in the construction sector.
[65]	Nordic countries	Assessed how recent energy policy changes in Nordic countries influence the environmental effects of construction waste, highlighting the need for more sustainable waste management practices.

3.1. Literature Analysis

A keyword search was conducted using Scopus and Google Scholar engines to collect CW-related articles. The search keyword terms included (construction waste, waste sources, construction waste causes, and waste management). The authors thoroughly scanned the abstracts to check the articles’ fitness for the search goal. This study includes articles that analyze CW’s causes; therefore, publications that target other goals, such as CW management, quantification, or waste recycling, are not included in the analysis. Sixty articles were chosen from the retrieved publications to cover the years (1995–2025), as shown in Table 2.

Table 2. List of articles included in the SNA.

Year Range	Articles	Number
1995–2000	[66,67,68,69,70,71]	6
2001–2010	[20,23,35,48,57,72,73,74,75,76,77,78,79]	13
2011–2025	[4,8,14,27,34,37,41,44,46,47,49,50,52,53,54,56,58,61,65,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101]	41

3.2. CW Source Categories and Factors

Osmani et al. [76] classified the main categories of CW sources according to the construction lifecycle stages, starting from the contract, design, procurement, site operation, and other phases connected to on-site transportation, material handling, and storage. The current study developed a more detailed classification of CW categories, as shown in Figure 3. The authors divided CW root sources into three main sections: preconstruction, construction, and managerial-related causes. CW could result from insufficient designs, procurement agreements, or unclear contractual documents. During the construction, waste could result from unskilled labor, inappropriate storage or transportation of materials, or unforeseen accidents.

Figure 3. Main CW source categories.

Following a comprehensive analysis of 60 literature sources, the authors identified 25 key construction waste source factors, as shown in Table 3. Some of the listed factors include multiple CW sources. For instance, factor F13 (Improper material storage, sorting, or handling) incorporates all aspects of material handling, including improper material storage, incorrect mixing quantities, casting, and curing with insufficient experience.

Table 3. CW root source factors explanation.

Category	ID	Factor (Original)	Description
Documentation	F1	Contract documentation error and incompleteness	Most construction contracts ignore CW management clauses. Errors and incompleteness at the project start also generate CW [93].
Design	F2	Design inaccuracies, Complex detailing	One-third of CW arises from design decisions, primarily when changes or poor technical understanding exist [76].
	F3	Design changes	Last-minute changes from clients or other stakeholders often require rework, generating CW.
	F4	Poor coordination and communication	Poor coordination among design and construction teams causes errors and overlapping work [71].
Procurement	F5	Over and under-ordering	Ordering excess or insufficient quantities causes material waste or delays.
	F6	Ordering errors and supplier errors	Errors in quantity/type or supplier mistakes lead to wrong deliveries, generating waste.
	F7	Unclear specifications	Vague specifications cause low-quality or unsuitable materials, resulting in rework and waste.
Higher Management	F8	The negative attitude of higher management	Lack of early stakeholder involvement and undefined roles contribute to CW generation.
On-Site Management	F9	Lack of supervision	Weak supervision allows for poor labor practices and improper material use.
	F10	Lack of safety precautions	Unsafe practices lead to material damage and accidents.
	F11	Lack of on-site waste management plans	The absence of formal waste management increases uncontrolled waste generation.
	F12	Improper planning for required quantities	Poor material forecasting leads to overordering or shortages.
Materials	F13	Improper material storage, sorting, or handling	Materials stored or mismanaged deteriorate or become unusable.
	F14	Depositing materials in unappropriated places	Materials placed in unsuitable areas may be damaged or cause obstruction.
	F15	Residual (Reinforcement, formworks, finishing works, concrete)	Cutoffs, leftovers, or unused material arise from inefficient usage or estimation.
	F16	Packaging waste	Excessive or unmanaged packaging contributes to physical waste on-site.
Transportation	F17	Improper transportation handling, Loading, and Unloading	Poor loading/unloading methods lead to physical damage to materials.
	F18	Delivery difficulties accessing the construction site	Difficult access results in delayed and risky handling, increasing the chances of damage.
	F19	Poor on-site transportation from storage	Inefficient or careless movement of materials leads to breakage or wastage.
Manpower	F20	Workers’ lack of incentives or proper guidelines	Unmotivated workers may neglect best practices, leading to careless waste.
	F21	Workers’ lack of training and poor craftsmanship	Inadequate training results in frequent errors and material misuse.
	F22	Poor work ethic	Negligence and carelessness cause unnecessary waste.
	F23	Accidents due to negligence	Site incidents damage materials and delay progress.
External	F24	Theft and vandalism	Security lapses lead to material loss through theft or destruction.
	F25	Unforeseen situations and accidents	External events like rain or storms damage materials or cause delays that result in waste.

It is important to clarify that the scope of this study is primarily oriented toward physical and material-related sources of construction waste. This focus is justified by their prevalence in the reviewed literature and actual site practices and their suitability for frequency-based analysis using SNA methodologies.

3.3. Developing the Social Network Analysis

SNA is the approach utilized for quantitative analysis in this study. It is a mathematical approach based on graph theory to study how various factors behave, accounting for their interconnectivity on a graphed network [102]. It comprises vertices connected by edges. Degree centrality (DC) is an essential characteristic of SNA that identifies the number of edges connected to each vertex [103]. Nodes with the highest edge connections have a high DC to give a more focused analysis of quantitative measures. In this study, DC was selected as the primary metric for evaluating the importance of construction waste source factors within the network. DC is widely adopted in prior SNA-based research due to its simplicity, interpretability, and robustness in identifying nodes with the most direct connections. The choice of DC as the primary centrality measure is grounded in its proven relevance for identifying highly connected nodes in factor-based SNA models, as reported in previous construction management research [104]. According to [104,105], it effectively captures the relative prominence of actors or factors in knowledge and influence dissemination.

Additionally, the consistency of results obtained from DC with known industry insights supports the reliability of the applied centrality measures in this context. DC offers a straightforward interpretation of the number of direct connections a factor has, which is particularly suitable for mapping interdependencies among CW source factors. While alternative measures such as betweenness and closeness centrality provide complementary perspectives, DC was prioritized here due to its alignment with the study’s objective of ranking factors by their direct influence within the network.

The SNA analysis starts with defining a reference matrix R, with 25 rows representing the extracted source factors, and 60 columns representing the retrieved articles. Each cell has a value of 1 if the factor in its row is mentioned in the corresponding article; otherwise, it has a value of 0 (Figure 4).

Figure 4. An example of a reference matrix.

An adjacency matrix was formed by multiplying the reference matrix by its transpose and replacing the resultant matrix diagonal values with zeros [104]. DC is a value calculated for each factor to reflect its importance when considered in previous discussions. The DC of each CW source factor is calculated from the developed adjacency matrix according to Equation (2). To ensure the reliability of the adjacency matrix, all factor-to-factor relationships were systematically extracted from the coded dataset, verified for consistency with the literature coding scheme, and checked for duplicate or contradictory links. This validation step ensured that the network accurately reflected the conceptual relationships identified during the literature review, thereby improving the robustness of the SNA results.

$${DC}_i=\sum _j{V}_{i,j}$$

(2)

where DC_i is the degree centrality for each factor i, and V_j is the cell value in row i and column j of the developed adjacency matrix.

3.4. Comparative Analysis

Datta et al. [59] identified 28 potential construction waste sources and analyzed these source factors using a questionnaire-based survey among construction professionals. They determined the sources that affect CW production most by calculating the factors’ relative importance index (RII). The analysis indicated that depositing construction materials in public places is the most influential factor. In addition, improper storage, poor training of workers, residual solid waste, and the lack of proper guidelines and supervision for workers are all considered significant CW sources. These factors from [59] represent the industry experts’ practical point of view, while the current study factors represent the theoretical and literature considerations for the CW source factors. For a more effective analysis, this study compares its extracted source factors with those considered by [59] to present some gaps between theoretical and practical factors defined by construction professionals and researchers. Table 4 lists the current factors and their similarities to those of [59]. To perform this comparison, the authors calculated a normalized score for each factor according to the RII calculated by [59] and DC from the SNA (Table 3).

Table 4. Comparing CW considered source factors.

	Current Study Factors	Corresponding Factors by [59]
F1	Contract documentation error and incompleteness	Not Considered
F2	Design inaccuracies, Complex detailing	Inaccuracies in design
F3	Design changes	Change in design by the owner, change in design by the architect
F4	Poor coordination and communication	Not Considered
F5	Over and under-ordering “Poor Supply Chain”	Over and under-ordering
F6	Ordering errors and supplier errors	Unpacked supply
F7	Unclear specifications	Not Considered
F8	The negative attitude of higher management	The negative attitude of higher management
F9	Lack of supervision	Not Considered
F10	Lack of safety precautions	Lack of safety precautions
F11	Lack of on-site waste management plans	Not Considered
F12	Improper planning for required quantities	Default packaging
F13	Improper material storage, sorting, or handling	Deposited material in a public place
F14	Depositing materials in inappropriate places	improper material storage
F15	Residual (Reinforcement, formworks, finishing works, concrete)	Formwork management, cutting the reinforcement, solid waste, finishing works, plastic works, tile works, metalwork, chemical waste, glasswork, and organic materials
F16	Packaging waste	Packaging Waste
F17	Improper transportation handling, loading, and unloading	Improper handling during transportation
F18	Difficulties with delivery vehicles accessing the construction site	Not Considered
F19	Poor on-site transportation from storage	Not Considered
F20	Workers’ lack of incentives or proper guidelines	Lack of incentive for employees
F21	Workers’ lack of training and poor craftsmanship	Absence of proper guidelines for workers
F22	Poor work ethic	Not Considered
F23	Accidents due to negligence	Not Considered
F24	Theft and vandalism	Theft and vandalism
F25	Unforeseen situations and accidents	Unforeseen situations (act of God)

4. Analysis of Results and Discussion

4.1. SNA Results

As detailed earlier in Section 3, Research Methodology, 60 literature articles covering the years 1995–2025 were retrieved and analyzed. Interest in construction waste research has increased exponentially in the last ten years. This noticeable rise is a consequence of the global demand to maintain sustainable industries. Figure 5 illustrates the resulting network of the keywords’ co-occurrence within the studied literature. The figure shows the main homogeneous domain of the articles covering all aspects of CW.

Figure 5. Keywords—Social Network Analysis.

A total of 25 source factors were identified with their corresponding categories (Table 2). After building the adjacency matrix in an Excel file, it is exported to the SNA environment (Gephi 0.9.2 version 3.0). Figure 6 shows the social network of the factors’ weighted DC scores regarding node size. As shown in the figure, each factor is represented as a node. Each node has various sizes that depend on the calculated weighted DC, and the edge between each node indicates that the two nodes were studied in the same literature article. The analysis shows that factor “F11,” the absence of CW management plans, is a grave source of waste on-site, as it has the highest DC score in the network.

Figure 6. Social network of the studied factors.

Residual (reinforcement, formworks, finishing works, concrete), workers’ lack of training, poor craftsmanship, and design inaccuracies with complex detailing are all significant CW sources. Table 5 represents the seven highest-ranking factors according to their DC from the SNA and their corresponding categories. The results indicate that most reviewed articles considered factor “F11,” the lack of on-site management plans, as an essential source of CW. However, factors “F23” (Accidents due to negligence) and “F1” (Contract documentation error and incompleteness) have the lowest DC in the network, indicating a lack of research studies incorporating these factors. Figure 7 shows the category ranking of the source factors, where materials, on-site management, manpower, and design were the most influential categories in generating CW.

Table 5. Top seven ranked CW generation factors.

Rank	Factor’s Label	Category	ID
1	Lack of on-site waste management plans	On-Site Management	F11
2	Residual (Reinforcement, formworks, finishing works, concrete)	Materials	F15
3	Workers’ lack of training and poor craftsmanship	Manpower	F21
4	Design inaccuracies, Complex detailing	Design	F2
5	Workers’ lack of incentives or proper guidelines	Manpower	F20
6	Depositing materials in unappropriated places	Materials	F14
7	Design changes	Design	F3

Figure 7. Source factors category ranking.

4.2. Results of the Comparative Analysis

The comparative analysis aims to identify the factors ignored by construction practitioners and scientific research publications (Table 3). Datta et al. [59] applied the RII as a numerical value for each factor to represent its relative importance, and it is calculated by Equation (3). The RII has a value ranging from 0 to 1, where large RII values mean a significant contribution to the CW production on site.

$$RII={\displaystyle \frac{\sum W}{A\times N}}$$

(3)

where W is the factor weighting from respondents (1 to 5), A is the highest scale weight (equal to 5), and N is the respondent’s number.

Figure 8 represents a histogram of the two studies calculated normalized scores of construction waste source factors. The authors calculated a normalized score for each factor according to the RII calculated by [59] and DC from the SNA (Table 6). The differences between the RII and DC normalized scores were also calculated to help indicate the gaps between the importance of factors within the two sides.

Figure 8. Normalized scores of SNA and [59] CW source factors.

Table 6. Normalized scores for source factors.

Source Factors	Normalized DC	Normalized RII	Differences
F1	0.59	-	0.59
F2	0.88	0.77	0.11
F3	0.81	0.84	−0.03
F4	0.77	-	0.77
F5	0.74	0.68	0.06
F6	0.69	0.74	−0.05
F7	0.73	-	0.73
F8	0.70	0.83	−0.13
F9	0.80	-	0.80
F10	0.65	0.83	−0.18
F11	1.00	-	1.00
F12	0.70	0.72	−0.02
F13	0.79	1	−0.21
F14	0.82	0.93	−0.11
F15	0.92	0.8	0.12
F16	0.73	0.83	−0.10
F17	0.70	0.81	−0.11
F18	0.70	-	0.70
F19	0.70	-	0.70
F20	0.85	0.77	0.08
F21	0.88	0.91	−0.03
F22	0.61	-	0.61
F23	0.50	-	0.50
F24	0.70	0.74	−0.04
F25	0.71	0.78	−0.07

When the negative values range increases, this indicates the need for more research to overcome these identified source factors (Table 6). On the other hand, when the range of positive values increases, project practitioners should pay more attention to this factor in construction projects. Figure 9 presents these differences to indicate the gap between the current and previous normalized values.

Figure 9. Derived differences between factors in SNA and [59].

While the comparative analysis highlights alignment on several key factors (e.g., improper material handling, residual waste), it also reveals notable discrepancies. For example, factors such as lack of supervision (F9), poor coordination (F4), unclear specifications (F7), and documentation errors (F1) were frequently discussed in the literature but rarely emphasized by practitioners. These gaps may stem from limited awareness among field personnel, the informal nature of site practices, and the absence of regulatory enforcement requiring documentation and communication protocols. Moreover, industry practitioners often prioritize tangible issues (e.g., packaging waste, logistics) over latent systemic causes (e.g., management culture or procedural frameworks), which receive more attention in academic discourse. This divergence underscores the importance of promoting knowledge transfer through targeted training programs, stakeholder workshops, and integration of waste-related Key Performance Indicators (KPIs) in project management dashboards. To address these discrepancies, the following mechanisms are proposed:

• Embedding CW-related clauses in contractual and regulatory documents to formalize responsibility.
• Mandating site-specific training on supervision, planning, and documentation best practices.
• Incorporating CW performance indicators into routine audits and project evaluations.
• Enhancing collaboration between academia and industry via joint knowledge-exchange forums.

This analysis not only explains the root causes of the discrepancies between literature and practice but also outlines actionable mechanisms to bridge these gaps, making the findings directly applicable to construction waste management in practice.

Also, the analysis points out that the lack of research studies on the identified factors is not significant, but it requires developing unified frameworks and strategies to deal with various waste sources. Also, more research is needed on some understudied factors, such as documentation errors, site accidents, and higher management involvement.

Following the analysis of Table 6, it is evident that certain CW source factors, such as the absence of on-site waste management plans, lack of supervision, and improper material handling, require more attention from research and industry practice. These findings emphasize the pressing need for integrated waste management frameworks that can be systematically applied across the construction sector.

In line with these observations, CE models increasingly shape emerging strategies for managing CW. These models promote closed-loop systems where construction and demolition waste is reused in subsequent projects, reducing reliance on virgin materials and minimizing landfill use. Recent initiatives, such as China’s 2023 Circular Development Plan, the EU’s CE Action Plan, and Australia’s National Waste Policy (2023), highlight how policy frameworks drive this shift. These efforts improve resource efficiency and align with the global push toward more sustainable construction practices [33].

While this study primarily employed DC to determine factor importance, future analysis could incorporate Betweenness Centrality (BC) to explore mediating roles of specific nodes, particularly those such as F15 (“Residual Materials”), which may bridge multiple CW source categories. This could provide deeper insights into system-level interdependencies within CW generation.

The associations reported in this study are based on network connectivity patterns within the literature rather than on direct statistical testing. While the results highlight potential relationships, future research using primary quantitative datasets could employ correlation and regression analyses to test and quantify these relationships statistically.

4.3. Conceptual Cross-Mapping with Lean Waste Typologies

Table 7 presents a conceptual cross-mapping between selected SNA-extracted factors and Lean Construction’s eight waste typologies (Muda) to extend the theoretical utility of the findings. While the study’s empirical focus remained on material-related waste, this mapping illustrates meaningful intersections with process inefficiencies traditionally addressed through Lean practices. These associations offer a foundation for future hybrid studies integrating SNA methods and Lean thinking in construction waste management.

Table 7. Cross-mapping between SNA factors and lean construction waste categories (Muda).

Lean Waste Type (Muda)	Definition (Lean Construction)	Associated SNA Factor(s)	Rationale
Overproduction	Producing more than is needed or earlier than required	F5: Over and under-ordering. F12: Improper planning for required quantities. F15: Residual material.	Surplus orders and inaccurate quantity planning generate excess materials and offcuts.
Waiting	Idle time due to material, labor, or information delays	F18: Delivery difficulties accessing the construction site. F6: Ordering errors and supplier errors. F3: Design changes. F4: Poor coordination and communication. F23: Accidents due to negligence.	Access and supplier issues, late design changes, poor coordination, and incidents stall workflows.
Transportation	Unnecessary movement of materials or tools	F17: Improper transportation and handling. F19: Poor on-site transportation from storage. F18: Delivery difficulties accessing the site.	Ineffective logistics and handling increase trips and material movement.
Inventory	Excess materials are stored on-site unnecessarily	F15: Residual. F16: Packaging waste. F5: Over and under-ordering. F13: Improper material storage or handling. F11: Lack of on-site waste management plans.	Over-ordering and weak storage/WM plans create stockpiles prone to damage.
Motion	Unnecessary movement of people	F14: Depositing materials in unappropriated places. F13: Improper material storage, or handling. F11: Lack of on-site waste management plans.	Poor placement/storage and absent WM plans force extra walking/searching.
Overprocessing	Doing more work than necessary or redundant work	F1: Contract error and incompleteness. F2: Design inaccuracies, complex detailing. F3: Design changes. F7: Unclear specifications. F21: Workers’ lack of training.	Documentation/spec/design flaws and low skills cause rework beyond what is needed.
Defects	Efforts caused by errors or mistakes	F2: Design inaccuracies, complex detailing. F21: Workers’ lack of training. F9: Lack of supervision. F17: Improper transportation handling. F10: Lack of safety precautions. F22: Poor work ethics.	Errors in design/execution and damage during handling yield defective outcomes and waste.
Underutilized Skills	Failure to leverage the workforce’s full capabilities	F21: Workers’ lack of training. F20: Workers’ lack of incentives or guidelines. F9: Lack of supervision. F8: The negative attitude of higher management.	Skills not developed or guided; weak supervision and management attitudes suppress workforce potential.

4.4. Mapping High-Centrality CW Factors to CE Pathways

While this study adopted a system-based approach to analyze CW sources, further conceptual integration with CE principles is crucial to support sustainable material cycles. CE strategies, typically defined as Reduce, Reuse, Recycle, and Recover, are central to minimizing material inputs and maximizing resource productivity in construction [61]. However, their practical operationalization within CW research, particularly in relation to high-impact waste sources, remains underexplored. To address this, a CE-material flow matrix (Table 8) is developed to cross-reference top-ranked SNA-derived factors (based on DC) with applicable CE strategies. For instance, F15 (Residual Materials) aligns directly with reuse and recycling loops and has high potential for industrial symbiosis when traceability systems are in place.

Table 8. Cross-mapping of high-centrality CW factors to CE strategies.

SNA Factor	Description	Mapped CE Strategy	Circular Mechanism
F11	Lack of on-site waste management plans	Reduce/Reuse/Recycle	Implementing comprehensive waste management plans can minimize waste generation by improving on-site practices and optimizing material flow.
F15	Residual materials (reinforcement, formworks, finishing works, concrete)	Reuse/Recycle	Residual materials can be tracked and recovered through digital tools (e.g., BIM), enabling their reuse or recycling in a closed-loop system.
F21	Workers’ lack of training and poor craftsmanship	Reduce/Underutilized Talent	Enhancing worker training can reduce errors and rework, thus lowering material waste and optimizing the use of human resources.
F2	Design inaccuracies and complex detailing	Reduce	Improving design quality minimizes errors and rework, thereby reducing unnecessary material consumption and waste generation.
F20	Workers’ lack of incentives or proper guidelines	Reduce	Better incentivization and clear guidelines can improve efficiency, reducing waste from improper practices.
F14	Depositing materials in unappropriated places	Reduce/Reuse	Improving storage practices and on-site handling minimizes damage, making materials more suitable for reuse or recycling.
F3	Design changes causing rework	Reduce	Minimizing design changes through early-stage validation and stakeholder coordination helps avoid re-work and thus reduces material waste.

This matrix reinforces the idea that high-centrality CW factors are not just operational inefficiencies but latent opportunities for circular innovation. Aligning CW source mitigation with CE pathways allows for strategic prioritization of material flows, facilitating closed-loop systems and reducing environmental burdens.

5. Main Guidelines and Recommendations

To bridge the identified knowledge gaps and promote evidence-based practice in CW management, this study offers both strategic recommendations and new perspectives for future research. Several actionable initiatives are proposed based on the SNA findings, particularly the high centrality of F11 (lack of structured waste management plans).

5.1. Standardized Waste Management Planning

The absence of structured, tiered CW management protocols emerged as a critical procedural deficiency. To address this, it is recommended that contractors be mandated to embed standardized CW management plans within project documentation. These plans should include the following:

• Assigned responsibilities across project teams.
• Phased implementation milestones aligned with construction stages.
• Key Performance Indicators (KPIs) to monitor material efficiency, waste reduction, and compliance with waste-related targets.

In parallel, the plans should encourage balanced procurement, supplier take-back schemes, site-wide segregation systems, warehouse-based surplus material storage, on-site recycling initiatives, and systematic post-construction clearance.

5.2. Digital Integration for Residual Material Traceability

While F11 reflects procedural gaps, F15 (residual materials) underscores the lack of technological integration in managing reusable components. Despite its relevance, F15 is often disconnected from digital tools. Adopting BIM-integrated residual material traceability systems is recommended, given the increasing validation of BIM for waste estimation and material reuse. These systems:

• Enable real-time tracking of excess or reusable materials.
• Trigger automated alerts for reuse or reallocation opportunities.
• Support closed-loop material recovery aligned with circular economy models.

These recommendations are supported by the recent literature:

• Liu et al. [5] developed a BIM-based decision framework to minimize CW at the design stage.
• Quiñones [58] introduced a BIM-enabled quantification tool for estimating material waste.
• Mitera-Kiełbasa and Zima [62] confirmed BIM’s ability to enhance coordination and material flow.

5.3. Enhancing Material Handling and Logistics

The comparative analysis confirmed that material storage, handling, and internal logistics remain underexplored yet significant contributors to CW. Site-specific storage strategies, supervision protocols, and real-time inventory systems should be prioritized, especially when integrated with BIM and IoT technologies. Future research is encouraged to explore the intersection of logistics, traceability, and waste dynamics.

5.4. Strategic Focus Areas for Future Research

To shift from disposal-based practices to process-integrated waste minimization, the following domains require focused academic and policy attention:

• Regulatory Frameworks and Governmental Incentives: Regulators must develop enforceable waste audit protocols, material reuse benchmarks, and transparent reporting mechanisms. Fiscal instruments—such as landfill levies, tax incentives, or procurement subsidies—should be empirically evaluated for their behavioral impact on industry practices.
• Executive Commitment and Institutional Leadership: CW minimization should be embedded into organizational policy from project inception. Future research could examine the impact of executive engagement on design coordination, procurement efficiency, and overall site sustainability performance.
• Design-Centered Waste Prevention: The design phase offers early intervention potential. Future inquiries should investigate how modular construction, design-for-disassembly, and the use of recycled-content materials can be systematically embedded without compromising aesthetics or structural integrity.
• Climate Adaptation and Resilience: Climatic variability contributes to material damage and delays. Research should examine how climate-resilient planning, predictive scheduling, and weather-adaptive storage systems can reduce waste under extreme conditions.
• Workforce Capacity, Training, and Ethical Culture: Human behavior remains central to on-site waste outcomes. Structured training in material handling, environmental ethics, and daily operational discipline should be institutionalized across project teams.

5.5. Toward a Unified Waste Management Framework

While this study employed a generalized SNA model to analyze CW source interrelations, future work could benefit from sector-specific SNA models, particularly for high-waste domains such as pavement construction, renovation, or demolition. These models could incorporate factors like asphalt reclamation defects and RAP recovery rates. Ultimately, the study advocates for a comprehensive, integrated CW management framework that enables the following:

• Synthesis of the most influential waste source factors.
• Leveraging digital tools such as BIM, RFID, and IoT for process monitoring.
• Alignment with regional regulatory, cultural, and operational conditions.

Such a model would serve as a decision-support system for project managers, policymakers, and researchers to operationalize circular economy principles and reduce environmental impacts across the construction lifecycle. To enhance the practical applicability of the proposed strategies, the following recommendations are organized according to governance level:

• Local level: Municipal authorities can implement incentive programs for on-site waste sorting, such as reduced landfill fees for projects achieving specified segregation rates, and enforce selective demolition permits requiring material recovery targets.
• National level: National governments can introduce tax reductions or subsidies for contractors and developers adopting CE practices, such as using recycled aggregates or integrating BIM-based waste tracking into project workflows. Public procurement policies should prioritize projects that demonstrate verifiable waste minimization outcomes.
• International level: Regional and global bodies (e.g., UNEP, ISO) can work toward harmonized CW reporting standards, cross-border certification systems for recycled materials, and collaborative knowledge-sharing platforms to exchange best practices and performance benchmarks.

This tiered approach provides stakeholders at each level with actionable measures aligned to their authority and resources, while fostering coordinated progress toward sustainable construction waste management.

5.6. Future Research Directions and Opportunities

Future research can build on this alignment by adopting CE performance metrics and simulating circular scenarios through dynamic systems modeling or material flow analysis. Although this study does not incorporate a comprehensive sensitivity analysis of the threshold parameter for edge inclusion due to data availability and temporal constraints, we recognize the significant influence such variation may have on the relative ranking of factors. Accordingly, future studies should undertake a systematic investigation of threshold sensitivity to better assess its impact on the robustness and reliability of factor prioritization within the proposed framework.

Furthermore, longitudinal study designs are recommended to investigate temporal variations in construction waste characteristics, including changes in waste types, quantities, environmental impacts, and the effectiveness of management strategies over time. Such dynamic analyses would provide deeper insights into the evolving nature of construction waste challenges and support the development of adaptive, time-responsive waste reduction frameworks aligned with the lifecycle of construction projects.

Given the scope, the present study adopted a generalizable factor-based analysis rather than conducting detailed case studies. While this approach enabled the identification of broadly applicable construction waste source factors, it does not capture context-specific variations across different projects or regions. Future research should therefore pursue case-based validation and expand the geographical scope to test the robustness and transferability of the findings.

Finally, expanding the scope beyond the peer-reviewed literature to include the grey literature, such as industry reports, government publications, and case studies, and non-English sources, could capture valuable practical insights and regional perspectives often absent from academic publications. Incorporating these broader sources would help produce a more comprehensive understanding of construction waste management practices across diverse contexts.

6. Conclusions

This research aimed to identify and analyze the most influential factors contributing to CW and provide evidence-based recommendations for mitigation. These conclusions are derived directly from the high-centrality factors identified through the study’s SNA and the thematic synthesis of the reviewed literature. The key conclusions drawn from the study are as follows:

• Thematic Mapping of CW Factors: A total of 25 CW source factors were extracted from the literature and categorized into major thematic domains, emphasizing material inefficiencies, design flaws, procedural gaps, and stakeholder-related issues.
• Social Network Analysis (SNA) Results: The SNA approach revealed that the absence of structured on-site waste management plans (F11) is the most central factor, followed by poor workmanship, inadequate training, and design inaccuracies, demonstrating systemic interdependencies.
• Implications for Site Practice: Improving material handling, site organization, and on-site logistics can significantly reduce CW. Contractor-led initiatives such as tiered waste management protocols and real-time material monitoring are essential.
• BIM and Circular Economy Integration: Integrating BIM and CE principles provides a viable pathway for proactive waste reduction. BIM-enabled traceability can improve material reuse, while CE strategies promote resource recovery and process efficiency.
• Research–Practice Gap: A disconnect remains between theoretical models and actual industry implementation. Bridging this gap requires the development of unified CW management frameworks supported by empirical data, digital tools, and regional policy alignment.
• Call for Collaborative Action: The findings of this study offer a strategic roadmap for construction firms, policymakers, and researchers to collaboratively advance CW minimization as a core component of sustainable construction, rather than a post-construction obligation.