Estimation Of Construction Waste Research Articles

The Cost of Urban Renewal: Annual Construction Waste Estimation via Multi-Scale Target Information Extraction and Attention-Enhanced Networks in Changping District, Beijing

Construction waste is an inevitable byproduct of urban renewal, causing severe pressure on the environment, health, and ecology. Accurately estimating the production of construction waste is crucial for assessing the consumption of urban renewal. However, traditional manual estimation methods rely heavily on statistical data and historical experience, which lack flexibility in practical applications and are time-consuming and labor-intensive. In addition, their accuracy and timeliness need to be improved urgently. Fortunately, with the advantages of high-resolution remote sensing images (HRSIs) such as strong timeliness, large amounts of information, and macroscopic observations, they are suitable for the large-scale dynamic change detection of construction waste. However, the existing deep learning models have a relatively poor ability to extract and fuse features for small and multi-scale targets, and it is difficult to deal with irregularly shaped and fragmented detection areas. Therefore, this study proposes a Multi-scale Target Attention-Enhanced Network (MT-AENet), which is used to dynamically track and detect changes in buildings and construction waste disposal sites through HRSIs and accurately estimate the annual production of urban construction waste. The MT-AENet introduces a novel encoder–decoder architecture. In the encoder, ResNet-101 is utilized to extract high-level semantic features. A depthwise separable-atrous spatial pyramid pooling (DS-ASPP) module with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. A dual-attention mechanism module (DAMM) is employed to better preserve positional and channel details. In the decoder, multi-scale feature fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. The MT-AENet is used to extract buildings and construction waste at different periods in the study area, and the actual production and landfill volume of construction waste are calculated based on area changes, indirectly measuring the rate of urban construction waste resource conversion. The experimental results in Changping District, Beijing demonstrate that the MT-AENet outperforms existing baseline networks in extracting buildings and construction waste. The results of this study are validated according to government statistical standards, providing a promising direction for efficiently analyzing the consumption of urban renewal.

Generalised Linear Modelling for Construction Waste Estimation in Residential Projects: Case Study in New Zealand

Construction waste is a global problem, including in New Zealand where it makes up 40–50% of landfill waste. Accurately measuring construction waste is crucial to reduce its impact on New Zealand’s landfills and meet carbon targets. Waste can be effectively managed if predicted correctly from the start of a project. Waste generation depends on factors such as geography, society, technology, and construction methods. This study focuses on developing a model specific to New Zealand to predict waste generation in residential buildings. By analysing data from 213 residential projects, the study identifies the design features that have the greatest influence on construction waste generation. A generalized linear model is constructed to correlate these design features with waste generation. The findings are valuable for construction stakeholders seeking to implement waste reduction strategies based on predicted waste quantities. This research serves as a starting point, and further investigation in this area is necessary.

Quantification of Construction Waste in Early Design Stages Using Bim-Based Tool

Construction and demolition waste represents a growing environmental, social, and economic problem, and has become a priority for European and worldwide policies. The early quantification of construction waste is essential for the minimisation of its production and the improvement of waste management. This requires the development of design-based tools that enable a better understanding of the expected waste produced during the construction phase. Building Information Modelling (BIM) methodologies have gained recognition in the Architecture, Engineering, Construction, and Operations (AECO) sector, largely due to their capacity for data simulation, storage, and management during the building design phase. This study presents a software application, called WE-BIM Add-in, to quantify construction waste (CW) while designing the BIM model in Revit. A validated CW quantification model which enables waste types and quantities per building element to be predicted in detail according to the European List of Waste (LoW) is integrated into the Revit workflow. Design alternatives could be effortlessly simulated in real time to assist practitioners in decision-making during the early design stages. Two alternative structural systems of a Spanish residential building were compared: a reinforced concrete structure, Option 1 (O1), and a steel structure, Option 2 (O2). The results were obtained automatically: O2, in addition to reducing 56% of O1′s waste, would have increased the waste recycling rate by 49%; and displayed in Revit, thereby remaining consistent with those of other studies that compare prefabricated systems with in situ systems. This work provides a basis for future research into the automated estimation of construction waste in BIM which could become a useful tool in waste-prevention policies.

Quantification of Construction Waste through BIM

Nowadays, green environment is a great concern in the construction industry because of its rapid growth in the construction sector and its direct connection with the detrimental impact on the environment. The construction sector contributes about 23% of air pollution, 50% of the climatic change, 40% of drinking water pollution, and 50% of landfill wastes. Developing country like Bangladesh did not use any proper waste estimation process or tools which can efficiently estimate the volume of materials needed for a construction project which always results in wasting excessive materials. Waste estimation can determine the volume of materials even in the pre-construction stage and thus reducing waste, cost and environmental impact. In the light of the gap of existing tools and methods to estimate the waste amount, Building Information Modelling (BIM) based system plays a vital role to estimate construction waste in the region of the rising amount of construction waste. In this paper, clash detection process has been described to estimate construction waste with the help of Autodesk Navisworks Manage software. Clash detection process contributes to estimate a large amount of waste among total waste generated in a building model. Different manual approaches have also been described that can estimate the total amount of construction waste. This paper also presents an example scenario with a 4-storied residential building model to demonstrate construction waste estimation systems. The case study shows that BIM-based waste estimation using clash detection could prevent 40-45% of construction waste that might have been generated without using BIM.

Estimation of construction waste composition based on bulk density: A big data-probability (BD-P) model

Estimating the composition of construction waste is crucial to the efficient operation of various waste management facilities, such as landfills, public fills, and sorting plants. However, this estimating task is often challenged by the desire of quickness and accuracy in real-life scenarios. By harnessing a valuable data set in Hong Kong, this research develops a big data-probability (BD-P) model to estimate construction waste composition based on bulk density. Using a saturated data set of 4.27 million truckloads of construction waste, the probability distribution of construction waste bulk density is derived, and then, based on the Law of Joint Probability, the BD-P model is developed. A validation experiment using 604 ground truth data entries indicates a model accuracy of 90.2%, Area Under Curve (AUC) of 0.8775, and speed of around 52 s per load in estimating the composition of each incoming construction waste load. The BD-P model also informed a linear model which can perform the estimation with an accuracy of 88.8% but consuming 0.4 s per case. The major novelty of this research is to harmonize big data analytics and traditional probability theories in improving the classic challenge of predictive analyses. In the practical sphere, it satisfactorily solves the construction waste estimation problem faced by many waste management facility operators. In the academic sphere, this research provides a vivid example that big data and theories are not adversaries, but allies.

An empirical study on waste generation rates at different stages of construction projects in China

Estimation of construction waste generation is critical to construction waste management decisions. However, current construction waste estimation methods have various limitations (e.g. small samples). To address those limitations, this research conducts an empirical study to evaluate the waste generation rate of different types of waste at different construction stages. In this study, construction waste from 148 new-built residential construction sites in China were sorted and weighted on site and their waste generation rates were estimated separately. The results indicated that the amount of inorganic nonmetallic waste with a generation rate of 16.59 kg m-2 was the highest among the five types of waste (i.e. inorganic nonmetallic waste, organic waste, metallic waste, composite waste, hazardous waste), while the waste generation rate for the underground construction stage, which was 27.57 kg m-2, was the highest among the three stages (i.e. underground stage, superstructure stage, finishing stage). Compared with previous data, the new waste generation rate proposed in this research can better estimate the actual waste generation situation in construction sites, which provides reliable information for proper decision-making. Furthermore, based on the result of the empirical study, some recommendations for construction waste reduction are proposed.

Methodology for upstream estimation of construction waste for new building projects

Construction waste includes waste arising from construction works such as new build, renovation, demolition and road works. Through waste sorting and separation, inert material can be used to fill in reclamation sites; however, the non-inert portion of the construction waste still goes to landfills. However, Hong Kong is running out of landfill space. When determining reduction and management strategies for construction waste, the Government's objectives are to reduce waste generation, maximize reusing and recycling and reduce the intake of mixed construction waste at landfills. In order to develop and implement the waste reduction and management strategies at project level, proper quantification of construction waste generation is a prerequisite. Our research reveals that limited information is currently available for understanding the magnitude and composition of construction waste and for preparing an appropriate construction waste management plan for a project on new or existing facilities. To bridge the gap, the aim of this research is to present a comprehensive and practical approach for estimating the construction waste for building projects once detail design is carried out for new build or major retrofitting works. The estimation of waste quantities is based on the actual design information available from tender or contract documentation for a project at the upstream stage (a full set of bills of quantities classified under standard format). The proposed methodology of aggregating wastages accruing to quantified elements for a complete building has its advantages over a ball park figure established through indexing or other methods as proposed in existing literature, in terms of ensuring accuracy and maximizing the use of available design information whilst the workflow is uninterrupted.Three representative case studies with different formwork and façade systems are depicted to highlight the significant effects on waste generation in otherwise similar construction projects.

BIM-based automated construction waste estimation algorithms: The case of concrete and drywall waste streams

Globally, the growth of construction activities over the past years has resulted in large quantities of waste generation. Much of this waste is not reused or recycled and is subsequently redirected to landfills. The environmental impact of construction waste (CW) generation and the shortage of land resources for the creation of new landfills have reinforced the need to adopt more innovative CW management practices. Estimation of CW is a necessary step for the adoption of CW management practices. In this study, Building Information Modeling (BIM) is used to automate CW quantification. In this context, CW generation is estimated as the materials purchased but not incorporated into the actual building structure. Algorithms developed to quantify concrete and drywall waste streams are presented to demonstrate the proposed CW estimation method. The proposed concrete algorithm is validated by comparing estimated waste to actual waste data reported in the waste hauling tickets of a real-world project. Furthermore, CW generation quantities reported in the literature are used to validate estimates of both concrete and drywall waste streams. By leveraging material quantities directly from BIM—as opposed to manual estimations—CW estimation can be streamlined, enabling decision makers to implement more efficient construction waste management practices in the field.

Investigation of construction wastes generated in the Malaysian residential sector.

The construction sector is among the fastest growing sectors in Malaysia; it consumes a vast amount of natural resources and produces a massive volume of construction and demolition waste. The waste is collected in a decentralised manner by sub-contracted companies. It is challenging to obtain reliable information on the amount of construction waste generated, because it is hard to determine its exact quantity and composition. Therefore, this study proposes a quantitative construction waste estimation model for residential buildings according to available data collected from the Construction Industry Development Board, Malaysia. In the development of this model, a theoretical investigation of the construction procedure and the construction waste generation process was conducted. The waste generated rate was determined as 25.79 kg m-2 for new residential constructions, which translates into about 553,406 t of anticipated waste annually.

Construction waste estimation depending on urban planning options in the design stage of residential buildings

In the framework of the integral construction of urban residences and buildings is necessary a previous study to analyze the evaluation and management of waste generated throughout the building process. This key ingredient of urban planning responds, in part, to growing environmental problems and a more acute awareness of the consequences that improper management of such wastes would entail. A previous quantification of the waste generated through construction – during the project stage – is needed so that the best building proposal may be chosen. Urban planners and policy makers should develop a keen eye for selecting cost-effective projects while environmentally friendly.The aim of this paper is to study the production of waste in light of diverse urban solutions, both in the urban planning and building stages, as well as in global terms. To this end we studied six types of housing projects through simulations using statistical data, for different purposes, but with a common construction surface (50,000m2): (i) detached single-family unit; (ii) semi-detached single-family unit; (iii) 5-floor apartment block; (iv) 10-floor apartment block; (iv) 20-floor apartment block; and (vi) 40-floor apartment block. The main finding is that linear constructions generate a greater volume of waste than vertical construction, the difference reaching up to 57%.

Developing a quantitative construction waste estimation model for building construction projects

Construction and demolition waste is a major source of urban solid waste, frequently accounting for 10–30% of the total waste disposed of at landfills in many cities around the world. There is a recognized need to manage the construction waste in order to maintain a sustainable environment. The successful implementation of construction waste management depends on a number of factors. One critical factor is the need to accurately estimate waste generated from construction projects. In this regard, this paper proposes a quantitative construction waste estimation model for building construction projects, which can enhance the accuracy in the estimation of construction waste at the project level. In the development of this model, a theoretical analysis of the construction process and the construction waste generation process has been conducted. Specifically, this model integrates the mass balance principle, work breakdown structure, material quantity takeoff, conversion ratios between different waste measurement units, and the wastage levels of various materials used in different work packages. The proposed model is able to predict the quantities of various kinds of construction waste from a building project, to track the origin of construction waste (i.e., from which work package a particular kind of waste is generated and how much) and to help contractors investigate the potential improvements for waste management. An illustrative example is provided to demonstrate the application of this quantitative construction waste estimation model.

Web-based construction waste estimation system for building construction projects

This paper proposes a web-based construction waste estimation system (WCWES) for building construction projects incorporating the concepts of work breakdown structure, material quantity takeoff, material classification, material conversion ratios, material wastage levels, and the mass balance principle. The WCWES integrates online data input modules and online analytical modules for the quantification of different kinds of waste generated in the construction process at the project level. It facilitates accessibility, interfacing, connectivity and information sharing of users in carrying out a wide range of construction waste estimation tasks for sustainable construction waste management. A hypothetical building construction project is used to demonstrate the application and usefulness of the WCWES. • Developing a web-based building construction waste estimation system • Allowing online analytical processing of construction waste • Improving the estimation accuracy of construction waste • Allowing multi-dimensional analysis of construction waste