ChapterPDF Available

Handbook of Research on Water Sciences and Society



Construction is an activity that fulfills one of the basic needs of humans (i.e., shelter). However, this industry is also known for its excessive waste generation, which impacts the environment if not disposed of appropriately. Much of the waste consists of harmful material; when dumped in the landfills, this leads to gradual leaching of various undesirable ions into the groundwater, causing water quality deterioration. Such leachate-rich water when used by humans for various purposes causes diseases and deformities. Thus, to improve the ecological civilization and to promote the overall ESG proposition in the construction industry, artificial intelligence (AI) is a suitable solution. This chapter puts forward a case study of an AI-based ConTech startup, called viAct that developed and tested AI modules for monitoring waste generation and disposal at construction sites. The AI modules are trained and tested for their efficacy in various construction sites for illegal dumping detection and classification of different types of waste material before they are discharged in landfill areas.
Handbook of Research
on Water Sciences and
Ashok Vaseashta
International Clean Water Institute, USA & Transylvania University of Brasov,
Romania & Academy of Sciences of Moldova, Moldova
Gheorghe Duca
be Institute of Chemistry, Moldova State University, Moldova & Academy of
Science of Moldova, Moldova & Romanian Academy, Romania
Sergey Travin
N. N. Semenov Federal Research Center for Chemical Physics, Russia
A volume in the Advances in Environmental
Engineering and Green Technologies (AEEGT)
Book Series
Published in the United States of America by
IGI Global
Engineering Science Reference (an imprint of IGI Global)
701 E. Chocolate Avenue
Hershey PA, USA 17033
Tel: 717-533-8845
Fax: 717-533-8661
Web site:
Copyright © 2022 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in
any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher.
Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or
companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark.
Library of Congress Cataloging-in-Publication Data
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the
authors, but not necessarily of the publisher.
For electronic access to this publication, please contact:
Names: Vaseashta, A. (Ashok) editor. | Duca, Gheorghe, 1952- editor. |
Travin, S. O. (Serge Olegovich) editor.
Title: Handbook of research on water sciences and society / Ashok
Vaseashta, Gheorghe Duca, Sergey Travin.
Description: Hershey, PA : Engineering Science Reference, [2022] | Includes
bibliographical references and index. | Summary: “The purpose of this
reference book is to serve as a compendium of all available knowledge of
Science of Water and Society, complete with a detailed index, as well as
numerous adjuncts such as bibliographies, illustrations, lists of
abbreviations and foreign expressions”-- Provided by publisher.
Identifiers: LCCN 2021017680 (print) | LCCN 2021017681 (ebook) | ISBN
9781799873563 (hardcover) | ISBN 9781799873570 (ebook)
Subjects: LCSH: Water--Encyclopedias. | Water quality. | Sewage.
Classification: LCC GB655 .E56 2022 (print) | LCC GB655 (ebook) | DDC
LC record available at
LC ebook record available at
This book is published in the IGI Global book series Advances in Environmental Engineering and Green Technologies
(AEEGT) (ISSN: 2326-9162; eISSN: 2326-9170)
Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter 31
DOI: 10.4018/978-1-7998-7356-3.ch031
Construction is an activity that fulfills one of the basic needs of humans (i.e., shelter). However, this
industry is also known for its excessive waste generation, which impacts the environment if not disposed
of appropriately. Much of the waste consists of harmful material; when dumped in the landfills, this leads
to gradual leaching of various undesirable ions into the groundwater, causing water quality deterioration.
Such leachate-rich water when used by humans for various purposes causes diseases and deformities.
Thus, to improve the ecological civilization and to promote the overall ESG proposition in the construc-
tion industry, artificial intelligence (AI) is a suitable solution. This chapter puts forward a case study of
an AI-based ConTech startup, called viAct that developed and tested AI modules for monitoring waste
generation and disposal at construction sites. The AI modules are trained and tested for their efficacy
in various construction sites for illegal dumping detection and classification of different types of waste
material before they are discharged in landfill areas.
Articial Intelligence as Virtual
Inspector for Construction
Waste Dumping:
Case Study of ViAct
Gary Ng
viAct, Hong Kong
Hugo Cheuk
viAct, Hong Kong
Surendra Singh
viAct, Hong Kong
Baby Sharma
viAct, Hong Kong
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
The construction industry, from the very beginning, has been non-eco-friendly (Yu et al., 2013; Yuan
et al., 2012). Constructions result in development and deterioration of land, depletion of resources,
generation of wastes and several other forms of pollution (Ofori et al., 2000; Tam et al., 2006). Various
construction activities like demolition, renovation of buildings and new construction, generate various
waste materials, such as rubbles and others. Waste generation may take place both during the extrac-
tion and the processing of the raw materials as well as the consumption of the final product thereafter
(Nowak et al., 2009). The construction culture has always contributed to generating wastes, yet the trade
contractors have been from time to time rewarded for their speed, keeping aside the impact, that their
work has caused on the environment (Sullivan et al., 2010). Further, the construction industry consumes
huge amounts of material and energy and at the same time generates a large number of solid wastes
(Yuan et al., 2012). Statistics show that globally the construction industry consumes 25% of virgin
wood and 40% of raw stone, gravel, and sand every year (Kulatunga et al., 2006). 40% of the extracted
materials are consumed in the production of building materials and construction itself (Kibert & Ries,
2009). Apart from this, 35% of industrial waste in the world is generated by the construction industry
(Hendriks & Pietersen, 2000; Solis-Guzman et al., 2009). In the European Union itself, the construction
industry generates wastes that amount 2-5 times of the household wastes (Nowak et al., 2009). Accord-
ing to the Rethinking Construction Report by Sir Egan, in the average construction industry, up to 30%
of all the construction work is reworked, laborers are utilized to half of their potential, and a minimum
of 10% of the building materials from every construction project is wasted (Egan, 1998). The wastes
generated by the construction industry have severe environmental, economic as well as social impacts.
The environmental impacts include contamination of soil and water, and deterioration of the landscape
due to uncontrolled landfills (Leiva et al., 2005). Further, the waste of materials brings about economic
costs to the construction industry, since new purchases are to be made to replace the waste materials, and
at the same time, the cost of rework, delays, and disposal bring about financial losses to the contractor
(Ekanayake & Ofori, 2000). Similarly, the social impact of construction waste includes the health and
the safety of the workers, and the image of the construction industry in the society (Yuan et al., 2012).
Reduction of construction wastes gains top priority among the waste management options, such as reduc-
tion, recycling, and disposal (Yu et al., 2013). The earlier studies conducted in the field of reduction of
construction wastes emphasized the direct observation of waste generation (Formoso et al., 2002), the
attitude of the operators towards waste reduction (Teo & Loosemore, 2001), and shorting and weighing
of waste materials (Bossink & Brouwers, 1996). Recycling has a very important role to play in preserv-
ing the areas for urban development in future, and at the same time, improving the quality of the local
environment (Kartam et al., 2004). Apart from recycling, inert end-of-life materials can be used for certain
purposes such as filling materials for land reclamation (Poon et al., 2001). The construction wastes have
a very high recovery potential. 80% of the total construction waste can be recycled (Bossink & Brouw-
ers, 1996). Various countries such as Belgium, Denmark, and the Netherlands have been successful in
achieving the aforementioned recycling rate, especially given the scarcity of raw materials and disposal
sites (Erlandsson & Levin, 2005; Lauritzen, 1998). In the year 2006-07, Australia disposed of around 7
million tons of construction and demolition waste at the landfills, 42% of which included construction
wastes (EPHC, 2013). Similarly in 2005, the UK generated a total of 89.6 million tons of construction
and demolition wastes, out of which 28 million tons were sent to the landfills (DEFRA, 2013). The
Asian countries have not been an exception to this trend. In Asian countries, such as Singapore, Hong
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Kong and Philippines, construction waste disposal has become a worrisome social and environmental
problem, owing to the adoption of disproportionate disposal approach (Yu et al., 2013).
Thus, Artificial Intelligence or AI has emerged as a much-needed solution to improve the ecology and
to promote the overall ESG proposition in the construction industry. To prove this, the chapter presents a
special case study of an AI-based ConTech startup, viz., viAct (Hong Kong). viAct has made and tested
AI-enabled computer vision to be employed in construction sites. The AI module has been well-trained
and tested in various construction sites, to find out its efficiency in illegal detection and classification
of different types of waste materials before they are dumped in the landfill areas. The module further
demonstrates that the monitoring, assessment, and early detection of waste generation and dumping using
AI, have great potential in controlling the groundwater pollution from the construction sites. Therefore,
the application of AI has both scientific and reasonable guiding significance as well as practical value
for promoting the sustainable construction business, in turn enhancing the Environmental, Social and
Governance (ESG) proposition of the sector.
The construction managers have to take a look at various waste management options, such as reduction,
recycling, and disposal of waste; if they want to control construction wastes. ‘Source segregation’ is at
the top of the waste management options. But source segregation is only possible when there is in-hand
knowledge of the various sources of waste. Thus, this section of the chapter provides an overall back-
ground of the various sources and the types of construction wastes, and their impact on groundwater.
Any kind of construction or demolition activity gives rise to construction and demolition wastes.
The wastes generated from the construction activities may be of different types, such as debris from
the demolition of building(s), earth materials, steel wastes, timber wastes, and concrete wastes (Yu et
al., 2021). Further, the construction wastes also include the mixed site clearance construction materials
produced from different construction activities, such as site clearance waste, land excavation or formation
of site, waste from demolition activities, building construction and renovation wastes, and road work
waste. These construction wastes have tremendous negative impacts on the environment, which include
ecological imbalance, decrease in environmental resources, changes in the living environment, and exces-
sive use of energy (Kabirifar et al., 2021; Newaz et al., 2020). Therefore, to reduce the harmful impact
of construction wastes on the environment, it is necessary for construction managers to be well aware
of the various factors that give rise to construction wastes; before they get into construction activities.
In order to have a better understanding, the various sources of construction wastes along with the
reasons and the types of waste generated are categorized below in table 1
Three factors, namely, design, operational procurement, and material handling have been attributed
towards contributing to construction site wastes generation (Ekanayake & Ofori, 2000). It has been
estimated that, approximately 33% of on-site wastes is related to project design (Osmani et al., 2008).
Thus, segregation and reduction of waste is not and should not be the responsibility of the construction
company solely. Even the client and the designer can make eco-friendly choices in terms of programs
and designs (Bossink & Brouwers, 1996). Further, studies have indicated that the waste of materials that
take place during construction is much higher than the estimates made by the construction companies
(Formoso et al., 2002). Although a certain amount of construction wastes can be avoided, the potential
benefit of preventing the generation of wastes on-site is considerable. Further, the objectives of sustain-
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
able development also include within itself the objective of waste reduction, through segregation, which
includes both segregation at source, as well recycling to reduce the quantity and the risks involved (Yang
& Shi, 2000). Thus, the European Wastewater Catalogue (EWC) also categorizes the construction waste
into certain categories that need to be bifurcated before being disposed of into the landfills to prevent
groundwater contamination. They are divided as follows disposable, salvageable, and recyclable. There-
fore, having in-hand knowledge of the various sources and types of waste that has been generated from
the site is very essential for adopting any kind of mitigation measures.
The review of literature observed two potential sources of the contaminants that were found in the
groundwater, when construction and demolition (C&D) wastes were dumped into the landfills, without
segregation. These are:
Heavy Metals were Identified in the Leachate Generated from C&D Debris
Leachate is defined as “the liquid that has percolated through solid waste and has extracted, dissolved, or
suspended materials from it” (Mor et al., 2006). The presence of heavy metals was found in the leachate
that was generated from the construction and demolition debris (Negi et al., 2020; Naveen et al., 2018).
Amongst all, the three most prominent metals found were Boron, Manganese, and Arsenic (Powell et
al., 2015). Talking about Boron, it is used in the manufacturing of all kinds of wallboards (sheetrock,
gypsum board) that are disposed of at landfills. The amount of boric acid used in the manufacturing
process is between 0.03 and 0.15% by weight (US Borax), on average. Further, Manganese is one of the
major components in the production of steel, iron, and certain types of aluminum alloys. Apart from
being primarily used in the metallurgic industry, Manganese is also used in the pigments of paints and as
colorants for bricks (US EPA). Untreated painted wood, metal and bricks, when disposed release these
metal ions into the groundwater. Similarly, commercial use of arsenic is related to agriculture and the
production of pressure-treated lumber using chromate copper arsenite (CCA). These are used for perma-
nent foundation support beams for decks and playsets in construction sites, wood shakes, and shingles.
These when dumped into landfills with others, release arsenic ions that contaminate the groundwater.
Table 1. Classification of waste material into types with respect to the source
Waste types Source/Activity Waste material released
Excavation waste Excavation activity Soil, Sand, Gravel, Rock, Clay
Demolition waste Demolition activity of buildings Concrete with metal like iron, tile, marbles, wood, stone, brick, gypsum,
roofing material
Renovation waste Building, roadways railway
renovations Concrete, broken stones, metal, pebble
construction waste
Generated from almost every
other commercial, residential and
critical construction sites
Concentrate, plastic, glass, pebble, ceramics, paper, cloths, wood, brick,
Plaster of Paris, Bituminous mixtures, coal tar, and tar.
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Redox Reactions in Groundwater due to the Leachate Infiltration
In environmental systems such as an aquifer, microorganisms act as a catalyst in speeding up the oxida-
tion-reduction reactions. These microorganisms are generally immobile and are limited to the aquifer’s
solids. However, mobile varieties of microorganisms can also be found in aquifers. For their living,
these microorganisms metabolize dissolved organic carbon that is either present naturally or as a result
of contamination. The leachate that passes through the aquifers transports carbon, providing electron
donor for redox reaction. Under anaerobic conditions, iron, nitrate, carbon dioxide, and manganese act
as electron acceptors; on the other hand, under aerobic conditions oxygen acts as the electron acceptor
(Fetter, 1993). Metabolism of the carbon in the leachate by the microorganisms, deplete the available
oxygen in the groundwater. This causes a shift in the source of an electron acceptor. The metals ions
found in the groundwater then act as an electron acceptor for the redox reaction (Ololade et al., 2020).
After the depletion of oxygen, the next preferred source in the water body sediment is nitrate, which is
followed by manganese, iron sulphate, and carbon dioxide. Such reactions incorporating both oxidation
and reduction lead to the creation of an environment promoting the dissolution of elements that are usually
stable in an oxygen-rich groundwater as compared to oxygen starved groundwater (Powell et al., 2015).
Digitization technologies, such as AI, and their application in waste management, and especially in sorting
out of wastes, has a deterministic role to play towards the transformation of the economy into a circular
economy (UNSD, 2018). Computer vision, machine learning and robotics (based on optical recognition
and intelligent evaluation algorithms) can play a very important role in addressing the multiple barri-
ers that have been recognized. In fact, the potentiality of revolutionizing the design and the working of
the municipal waste sorting plants has increased with the advent of artificial intelligence (Asif et al.,
2007). Artificial intelligence can augment the efficiency of working and thus assist in more sustainable
management of municipal as well as industrial waste.
The waste sorting plants along with the disposal infrastructure play the role of a filter in the value
chain of waste management. They remove the material fractions that are more or less finely sorted,
which can either be diverted directly into production or can be sold as raw materials in the local or the
global market. These plants also reduced the amount of waste generated from the final disposal. Owing
to these reasons, the sorting out of construction wastes has become a relevant topic of research in recent
times. Generally, there are two technical approaches to sort out wastes into individual material streams.
These are manual sorting and automatic or mechanical sorting. AI ensures a considerable increase in
the sorting performance of category-wise waste streams. It could be particularly valuable in the case of
the waste streams that contain hazardous elements since it could provide precise sorting without human
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Problem Statement
The eleven South Asian countries, namely, Cambodia, Laos, Brunei Darussalam, Indonesia, Myanmar,
Singapore, Philippines, Vietnam, Malaysia, Thailand, and Timor-Leste have different levels of devel-
opment. The World Bank’s income-based development scale ranks Singapore and Brunei Darussalam
as high-income economies; Malaysia and Thailand as upper-middle income economies; and the rest of
the countries are ranked as the lower-middle-income group (World Bank, 2019). The current GDP per
capita of the Southeast Asian countries ranges from US$ 1,423 in Myanmar to US$ 64,103 in Singapore
(World Bank, 2019). The statistics of the Statista Research Department show that Southeast Asia has
experienced considerable growth of population in the last 10 years. It has been estimated that by the
year 2035, the regional population shall reach as much as 750 million, out of which more than 50% are
estimated to be residing in urban areas. In the year 2019, the construction industry has accounted for an
average of 5.2% of the total value added in the Southeast Asian countries. This trend of ever-increasing
urbanization has been and shall continue to contribute significantly towards the increase of urban devel-
opment and renovation activities, which in turn is increasing and will continue to increase the problem
of construction and demolition wastes (Jain, 2017; Othman et al., 2013). Thus, viAct have been work-
ing to promote an all-around construction safety and productivity solution for the South Asian nations
especially by taking initiatives in developing a variety of environmental modules of prime importance.
Organization Summary
viAct is the leading AI-enabled automated construction monitoring platform. viAct’s proprietary vision
technologies and extensive deployment experience can detect, anticipate the potential risks in construc-
tion sites 24*7 and trigger 5G enabled instant, real-time alerts for any non-compliance in construction
sites including non-compliances related to improper onsite waste classification. viAct’s well-trained AI
modules can cope-up with any kind of environment and operate even under extreme weather by con-
necting within 5 minutes to any type of online cameras, drones and mobile phones to capture video and
image without AI and coding knowledge. This facilitates construction companies to ensure environmental,
social & governance (ESG) compliances as well.
AI Powered Environment Modules of viAct
viAct has visualized this issue in three different perspectives and thus created two multi-dimensional
modules in this respect: one is associated with on-site waste classification management and the other is
associated with proper dump truck management for the proper carrying of waste from construction site
to destined landfills or recycle units. The following section explains the purpose and working of all the
modules and sub-modules in this context.
On-Site Waste Management Modules
Module Name: Waste Classification Module & Illegal Waste Dumping Module
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Module purpose: The proprietary AI of viAct’s waste classification module is trained so well that it
can identify and monitor the dumping of waste in construction sites. The module functions in multiple
aspects – firstly, it has been trained to classify various categories of waste into “inert” and “non-inert”.
The former is material like soil, sand, and eco-friendly debris; while, the latter is non-biodegradable
materials like plastic, metal which can either be reused or recycled. The AI is powerful enough to detect
any unexpected dumping of the waste into the wrong containers and gives sound alerts for immediate
remediation (Fig 1).
Moreover, the module is also applied on dump trucks carrying wastes so that classified loading of
waste is ensured before its disposal into landfills or disposal sites (Fig 2). Thus helps in manag-
ing an onsite accuracy of waste classification in construction sites.
Apart from this, a very common habit of getting rid of C&D waste is to dump it illegally in and
around the premises of construction sites. To monitor this, viAct has also trained it smart AI to detect
illegal dumping by giving audio-visual alerts for such scenarios.
Figure 1. Validation result for waste classification module
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Dump Truck (Carrying C&D waste) Management Modules
Module name: Dump Truck Cover Detection Module & Dump Truck Dirty Wheel Detection Module
Module Purpose: Being majorly utilized for carrying waste and loose materials, such as sand, gravel
and stone in and out of the construction site, they are expected to be closed while operational. However,
in the case of an open dump truck, falling of hazardous materials such as tar and asphalt may cause
hazardous accidents on roads. These contaminate the environment and damage the road surfaces as well.
Moreover, dirty wheels with C&D waste and other construction workplace associated material sticking
is another cause of unhygienic practice often observed (fig 4). Thus viAct’s modules are designed in a
way to detect uncovered dump trucks and dirty wheels before their release from the construction sites.
In case of such non-compliance, instant alert generated by the system helps managers to instantly take
suitable actions. Moreover, a complete surveillance of dump tracks is further achieved by the digitization
of dump truck license number and CHIT number (fig 5).
Figure 2. Validation result for dump truck C&D waste and inert material classification
Figure 3. Validation result for illegal dumping detection
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Figure 4. Validation of Dump truck cover detection module & Dump truck dirty wheel detection module
Figure 5. Digitization of waste carrying dump truck license number and CHIT number
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Working and Prerequisites of the Modules
The working of both the modules is dependent on the following pre-requisites: any IP camera with a
minimum resolution of 2 mega pixels, power supply, 5G/4G internet, AI processor, 7” monitor with sound
system. The viAct’s smart AI processors power the camera to monitor any wrongly dumped construction
waste. Either it is dumping in a wrong container or illegally dumping waste in unauthorized locations
or dump trucks carrying waste with unclassified waste and dirty wheels, in all the cases instant audio-
visual alert is generated, this is notified to the on-site manager for immediate action. Moreover, at the
same time, connecting to the viAct’s smart cloud instant alerts through emails and SMS are also sent to
the off-site stakeholders. The record of such incidents can also be visualized in the viAct’s dashboard
for future reference as well.
This helps in keeping a strict surveillance of the onsite waste disposal and dumping helping construc-
tion site mangers to properly manage C&D waste generated in their sites.
With the advancement in human civilization, the importance of the construction industry has been in-
creasing simultaneously. Construction is considered as an unavoidable activity, as it fulfills one of the
basic human needs, that is, ‘shelter’. In spite of its tremendous importance, the construction industry
has also been known and criticized for its non-eco-friendly nature. There are several activities related to
the construction industry, such as demolition which contributes towards the generation of huge quanti-
ties of waste. Moreover, waste in the construction industry is generated at every stage, starting from the
extraction of raw materials to the utilization of the final product. Statistics show that 35% of the world’s
industrial waste is generated solely in the construction sector. Thus, at present times, the management of
the construction and demolition (C&D) waste has become a very worrisome problem. Many research-
ers have suggested on-site segregation of C&D waste as the most feasible solution for its management.
However, the quality and quantity of these wastes make manual segregation out of question! But the
ever-increasing environmental issues caused by C&D wastes have generated the requirement of imme-
diate redressal. In this regard, Artificial Intelligence (AI) has emerged as the much-needed solution to
both improve the environment as well as to promote ESG criteria in the construction industry. A good
practical illustration to this is viAct’s AI-powered environmental modules which utilize AI-based com-
puter vision to monitor multiple aspects of C&D waste for construction projects.
Asif, M., Muneer, T., & Kelley, R. (2007). Life cycle assessment: A case study of a dwelling home in
Scotland. Building and Environment, 42(3), 1391–1394. doi:10.1016/j.buildenv.2005.11.023
Bossink, B. A. G., & Brouwers, H. J. H. (1996). Construction waste: Quantification and source evalua-
tion. Journal of Construction Engineering and Management, 122(1), 55–60. doi:10.1061/(ASCE)0733-
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
DEFRA, Department for Environment, and Food and Rural Affairs. (2013). Construction and demolition
waste management: 1999–2005. Author.
Egan, J. (1998). Rethinking Construction: The Report of the Construction Task Force to the Deputy
Prime Minister, John Prescott, on the Scope for Improving the Quality and Efficiency of UK Construc-
tion. Department for Trade and Industry.
Ekanayake, L. L., & Ofori, G. (2000). Construction material waste source evaluation. Proceedings of
the Strategies for a SustainableBuilt Environment.
EPHC. (2013). Characterization of Building-Related Construction and Demolition Debris in the United
States. US Environmental Protection Agency.
Erlandsson, M., & Levin, P. (2005). Environmental assessment of rebuilding and possible performance
improvements effect on a national scale. Building and Environment, 40(11), 1459–1471. doi:10.1016/j.
Fetter, C. W. (1993). Contaminant Hydrology. Macmillan Publishing Company.
Formoso, C. T., Soibelman, L., de Cesare, C., & Isatto, E. L. (2002). Material waste in building industry:
Main causes and prevention. Journal of Construction Engineering and Management, 128(4), 316–325.
Hendriks, C. F., & Pietersen, H. S. (2000). Sustainable Raw Materials: Construction and Demolition
Waste. RILEM Publication.
Jain, A. (2017). Summary report: waste management in ASEAN countries. United Nations Environment
Kabirifar, K., Mojtahedi, M., & Wang, C. C. (2021). A systematic review of construction and demolition
waste management in AUSTRALIA: Current practices and challenges. Recycling, 6(2), 34. doi:10.3390/
Kartam, N., Al-Mutairi, N., Al-Ghusain, I., & Al-Humoud, J. (2004). Environmental management of
construction and demolition waste in Kuwait. Waste Management (New York, N.Y.), 24(10), 1049–1059.
doi:10.1016/j.wasman.2004.06.003 PMID:15567670
Kibert, C., & Ries, R. (2009). Green building education and research at the University of Florida. Pro-
ceedings of the 45th ASC International Annual Conference.
Kulatunga, U., Amaratunga, D., Haigh, R., & Rameezdeen, R. (2006). Attitudes and perceptions of
construction workforce on construction waste in Sri Lanka. Management of Environmental Quality,
17(1), 57–72. doi:10.1108/14777830610639440
Lauritzen, E. K. (1998). Emergency construction waste management. Safety Science, 30(1-2), 45–53.
Leiva, C., Vilches, L. F., Vale, J., & Fern’andez-Pereira, C. (2005). Influence of the type of ash on the fire
resistance characteristics of ash-enriched mortars. Fuel, 84(11), 1433–1439. doi:10.1016/j.fuel.2004.08.031
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Mor, S., De Visscher, A., Ravindra, K., Dahiya, R. P., Chandra, A., & Van Cleemput, O. (2006). Induc-
tion of enhanced methane oxidation in compost: Temperature and moisture response. Waste Management
(New York, N.Y.), 26(4), 381–388. doi:10.1016/j.wasman.2005.11.005 PMID:16446082
Naveen, B. P., Sumalatha, J., & Malik, R. K. (2018). A study on contamination of ground and surface
water bodies By LEACHATE leakage from a landfill in Bangalore, India. International Journal of Geo-
Engineering, 9(1), 27. Advance online publication. doi:10.118640703-018-0095-x
Negi, P., Mor, S., & Ravindra, K. (2020). Impact of landfill leachate on the groundwater quality in three
cities of North India and health risk assessment. Environment, Development and Sustainability, 22(2),
1455–1474. doi:10.100710668-018-0257-1
Newaz, M. T., Davis, P., Sher, W., & Simon, L. (2020). Factors affecting construction waste manage-
ment streams in Australia. International Journal of Construction Management, 1–9. doi:10.1080/1562
Nowak, P., Steiner, M., & Wiegel, U. (2009). Waste management challenges for the construction industry.
Construction InformationQuarterly, 11(1), 8.
Ofori, G., Briffett, C. IV, Gang, G., & Ranasinghe, M. (2000). Impact of ISO 14000 on construction enterprises
in Singapore. Construction Management and Economics, 18(8), 935–947. doi:10.1080/014461900446894
Ololade, O. O., Mavimbela, S., Oke, S. A., & Makhadi, R. (2019). Impact of leachate from northern
landfill site in bloemfontein on water and soil quality: Implications for water and food security. Sustain-
ability, 11(15), 4238. doi:10.3390u11154238
Osmani, M., Glass, J., & Price, A. D. F. (2008). Architects’ perspectives on construction waste reduction
by design. Waste Management (New York, N.Y.), 28(7), 1147–1158. doi:10.1016/j.wasman.2007.05.011
Othman, S. N., Zainon Noor, Z., Abba, A. H., Yusuf, R. O., & Abu Hassan, M. A. (2013). Review on
life cycle assessment of integrated solid waste management in some Asian countries. Journal of Cleaner
Production, 41, 251–262. doi:10.1016/j.jclepro.2012.09.043
Poon, C. S., Yu, A. T. W., & Ng, L. H. (2001). On-site sorting of construction and demolition waste in Hong
Kong. Resources, Conservation and Recycling, 32(2), 157–172. doi:10.1016/S0921-3449(01)00052-0
Powell, J. T., Jain, P., Smith, J., Townsend, T. G., & Tolaymat, T. M. (2015). Does Disposing of Con-
struction and Demolition Debris in Unlined Landfills Impact Groundwater Quality? Evidence from 91
Landfill Sites in Florida. Environmental Science & Technology, 49(15), 9029–9036. doi:10.1021/acs.
est.5b01368 PMID:26130423
Solis-Guzman, J., Marrero, M., Montes-Delgado, M. V., & Ram’ırez-de-Arellano, A. (2009). A Spanish
model for quantification and management of construction waste. Waste Management (New York, N.Y.),
29(9), 2542–2548. doi:10.1016/j.wasman.2009.05.009 PMID:19523801
Sullivan, G., Barthorpe, S., & Robbins, S. (2010). Managing Construction Logistics. Blackwell.
Tam, W. Y. M., Tam, C. M., Chan, W. W. J., & Ng, C. Y. W. (2016). Cutting construction by prefabrication.
International Journalof Construction Management, 6(1), 15–25. doi:10.1080/15623599.2006.10773079
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Teo, M. M. M., & Loosemore, M. (2001). A theory of waste behavior in the construction industry.
Construction Management and Economics, 19(7), 741–751.
United States Environmental Protection Agency. (n.d.). Retrieved 28 July 2019 from https://www.epa.
UNSD. (2018). Value Added by Economic Activity National Accounts Section. Retrieved 28 July 2019
from a/Basic
World Bank. (2019). GDP per capita (current US$). Retrieved 28 July 2019 from https://data.worldbank.
World Bank. (2019). World Bank country and Lending Groups. Retrieved 28 July 2019 from https:// edgeb ase/articles/906519-world-bank-country-and-lending-groups
Yang, Y., & Shi, L. (2000). Integrating environmental impact minimization into conceptual chemical
process design—A process systems engineering review. Computers & Chemical Engineering, 24(2–7),
1409–1419. doi:10.1016/S0098-1354(00)00384-7
Yu, A. T., Wong, I., Wu, Z., & Poon, C.-S. (2021). Strategies for Effective waste reduction and manage-
ment of building construction projects in highly URBANIZED Cities—A case study of Hong Kong.
Buildings, 11(5), 214. doi:10.3390/buildings11050214
Yu, A. T. W., Poon, C. S., Wong, A., Yip, R., & Jaillon, L. (2013). Impact of construction waste dis-
posal charging scheme on work practices at construction site in Hong Kong. Waste Management, 33(1),
138–146. doi:10.1016/j.wasman.2012.09.023 PMID:23122205
Yuan, H., Chini, A. R., Lu, Y., & Shen, L. (2012). A dynamic model for assessing the effects of manage-
ment strategies on the reduction of construction and demolition waste. Waste Management (New York,
N.Y.), 32(3), 521–531. doi:10.1016/j.wasman.2011.11.006 PMID:22197665
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A.
A. (2020). Deep learning in the construction industry: A review of present status and future innovations.
Journal of Building Engineering, 32, 101827. doi:10.1016/j.jobe.2020.101827
Heiskanen, A. (2020, December 17). CEMEX ventures Launches TOP50 Contech startups. AEC Business.
Retrieved 28 July 2019 from
Klashanov, F. (2016). Artificial intelligence and Organizing decision in construction. Procedia Engineer-
ing, 165, 1016–1020. doi:10.1016/j.proeng.2016.11.813
Rahman, M. W., Islam, R., Hasan, A., Bithi, N. I., Hasan, M. M., & Rahman, M. M. (2020). Intelligent
waste management system using deep learning with iot. Journal of King Saud University - Computer
and Information Sciences. doi:10.1016/j.jksuci.2020.08.016
Articial Intelligence as Virtual Inspector for Construction Waste Dumping
Skibniewski, M. J., Wu, X., Pan, Y., & Zhang, L. (2021). Artificial intelligence in construction engineer-
ing and management. Springer.
viAct. (n.d.). viAct: Leading Construction AI company in Asia. Retrieved 28 July 2019 from
Youcai, Z. (2018). Leachate generation and characteristics. Pollution Control Technology for Leachate
from Municipal Solid Waste, 1–30. doi:10.1016/B978-0-12-815813-5.00001-2
Youcai, Z., & Sheng, H. (2017). Pollution control and resource recovery: Industrial construction and
demolition wastes. Butterworth-Heinemann, an imprint of Elsevier. doi:10.1016/B978-0-12-811867-
Yuan, H., & Shen, L. (2011). Trend of the research on construction and demolition waste management.
Waste Management (New York, N.Y.), 31(4), 670–679. doi:10.1016/j.wasman.2010.10.030 PMID:21169008
AI Cloud: A shared infrastructure for AI use cases, supporting numerous projects and AI workloads
simultaneously, on cloud infrastructure at any given point in time.
Artificial Intelligence: A technology that leverages computers and machines to mimic the problem-
solving and decision-making capabilities of the human mind.
Computer Vision: A field of artificial intelligence (AI) that enables computers and systems to derive
meaningful information from digital images, videos and other visual inputs — and take actions or make
recommendations based on that information. If AI enables computers to think, computer vision enables
them to see, observe and understand.
Construction and Demolition (C&D) Waste: The debris generated during the construction, renova-
tion and demolition of buildings, roads, and bridges.
ConTech: An amalgamation of technology in construction industry to increase efficiency and pro-
ductivity of construction work.
Industry 4.0: The fourth industrial revolution that represents a new stage in the organization and
control of the industrial value chain through intelligent networking of machines and processes with the
help of information and communication technology.
Sustainable Materials Management (SMM): An approach that identifies certain C&D materials
as commodities that can be used in new building projects, thus avoiding the need to mine and process
virgin materials.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Construction and demolition waste (C&DW) has a deleterious impacts on sustainability not only in developing countries but also in developed nations. For example, Australia generated more than 27 million tonnes of C&DW in 2018–2019; however, only 60% of this waste stream was recovered. Considering this low recovery rate, lower than many developed nations, and with regards to the increasing rate of C&DW generation, extra attention should be given to the construction and demolition waste management (C&DWM) in Australia. Therefore, this research attempts to accurately understand the current practices and challenges of C&DWM in Australia. To do so, primarily, a systematic review of studies relevant to C&DWM from 2010 to 2021 was performed. In this step, 26 research documents were meticulously analysed to identify the current practices of C&DWM in Australia. Then, an in-depth interview with three experts were undertaken to verify the major results and to investigate the challenges of C&DWM in Australia. The results indicated that three factors significantly affect C&DWM in Australia, namely attitudes and behaviour of C&DWM stakeholders, C&DWM in project life cycles, and C&DWM regulations with regards to sustainability, adding that the latter was revealed as the most effective in C&DWM in Australia.
Full-text available
Hong Kong is a densely populated city with high-rise developments, and as in other metropolitan cities, the amount of waste generated from construction projects in the city is increasing annually. The capacity of existing landfills is expected to be saturated by the 2020s. Construction waste management has been implemented for years but the performance is still not satisfactory. The aim of this research paper is to explore and formulate strategies and measures for effective construction waste management and reduction in highly urbanized cities such as Hong Kong. A desktop study on construction waste management practices was carried out for a preliminary understanding of the current situation in Hong Kong. Semistructured interviews and focus group meetings were further conducted to shed light on how to improve construction waste reduction and management in Hong Kong. The main contributions of this research study are the potential short-term, medium-term, and long-term strategies, which are related to the design stage, tender stage, construction stage, and government support. The five major strategies recommended are financial benefits to stakeholders, public policies in facilitating waste sorting, government supports for the green building industry, development of a mature recycling market, and education and research in construction waste minimization and management.
Full-text available
The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigated research in the application of advanced machine learning algorithms such as deep learning to help with diagnostic and prescriptive analysis of causes and preventive measures. However, the publicity created by tech firms like Google, Facebook and Amazon about Artificial Intelligence and applications to unstructured data is not the end of the field. There abound many applications of deep learning, particularly within the construction sector in areas such as site planning and management, health and safety and construction cost prediction, which are yet to be explored. The overall aim of this article was to review existing studies that have applied deep learning to prevalent construction challenges like structural health monitoring, construction site safety, building occupancy modelling and energy demand prediction. To the best of our knowledge, there is currently no extensive survey of the applications of deep learning techniques within the construction industry. This review would inspire future research into how best to apply image processing, computer vision, natural language processing techniques of deep learning to numerous challenges in the industry. Limitations of deep learning such as the black box challenge, ethics and GDPR, cybersecurity and cost, that can be expected by construction researchers and practitioners when adopting some of these techniques were also discussed.
Full-text available
Waste management leads to the demolition of waste conducted by recycling and landfilling. Deep learning and the Internet of things (IoT) confer an agile solution in classification and real-time data monitoring, respectively. This paper reflects a capable architecture of the waste management system based on deep learning and IoT. The proposed model renders an astute way to sort digestible and indigestible waste using a convolutional neural network (CNN), a popular deep learning paradigm. The scheme also introduces an architectural design of a smart trash bin that utilizes a microcontroller with multiple sensors. The proposed method employs IoT and Bluetooth connectivity for data monitoring. IoT enables control of real-time data from anywhere while Bluetooth aids short-range data monitoring through an android application. To examine the efficacy of the developed model, the accuracy of waste label classification, sensors data estimation, and system usability scale (SUS) are enumerated and interpreted. The classification accuracy of the proposed architecture based on the CNN model is 95.3125%, and the SUS score is 86%. However, this smart system will be adjustable to household activities with real-time waste monitoring.
Full-text available
Solid waste management in developing cities is a threat to water and food security. The final disposal option for solid wastes is usually landfill sites. Possible contaminants and their impact on surface and groundwater, and soil quality, at the northern solid waste landfill in Bloemfontein city, South Africa, was investigated. Soil samples were analysed for basic cations and heavy metals. A one-point surface leachate, groundwater, and surface water samples were analysed for physicochemical and microbiological parameters. Hydrochemical speciation models were developed using these parameters to determine the influence of the leachate emanating from the landfill on the quality of the water samples. Findings from the study showed that the low metal content in the soil and water samples posed no immediate threat to food and water security. However, most of the other parameters were above the permissible limit of South African National Standard 241 (SANS241) and World Health Organisation) (WHO for drinking water, a(nd the Department of Water Affairs and Forestry (DWAF) specification for irrigation, an indication that the groundwater was unfit for drinking, domestic and irrigation purposes. Metal concentrations in the soil also increased with distance downslope of the landfill along drainage lines. The implementation of a circular economy in Bloemfontein will translate to less pollution and enhance sustainable development.
Full-text available
This paper discusses the effects of a potential leachate leakage from a municipal solid waste landfill, situated at Mavallipura, Bangalore, India, on the surrounding water bodies. The landfill area is spread over an area of about 100 acres that began accepting waste from 2005. MSW was deposited in non-engineered manner that has resulting in steep and unstable slopes, leachate accumulation within the MSW mass, and leachate runoff into nearby water bodies such as ponds and open wells. The current study investigates the physicochemical characterization of landfill leachate and nearby water bodies. The batch leach tests were conducted to know the heavy metal concentrations in the contaminated soil. A series of column tests were also conducted to estimate the migration rates of different contaminants through the soil. Furthermore, these transport parameters were considered as input for fluidyn-POLLUSOL model to estimate the migration of leachate from the landfill site to the surrounding water bodies.
Full-text available
Landfill leachate has an adverse impact on groundwater quality as well as on living being. It contain high levels of organic, inorganic, heavy metal, and xenobiotics, which percolates through the subsoil and contaminate the groundwater. To assess the effect of landfills on groundwater, various physicochemical parameters including heavy metals, and microbiological examination of leachate and groundwater samples was conducted. The results obtained were compared with Bureau of India Standards and World Health Organization guidelines. The results of the study shows that the majority of the sample do not lie in the permissible limits. According to the results, the concenteration of ammoniacal nitrogen (9.8 mg/L), chemical oxygen demand (128 mg/L), chloride (115 mg/L), sodium (98 mg/L) and potassium (42.2 mg/L) was found relatively higher in water samples that have lower depth (30 ft) and distance (1 km) from the landfill. The concentration of measured parameters decreases with increase in depth and distance confirming that the leachate is the potential source of groundwater contamination. Hazard index of Chandigarh, Mohali, and Panchkula landfill site was 0.61, 0.53, and 0.01 mg/kg/day in pre-monsoon and 0.38, 0.24, and 0.01 mg/kg/day in post-monsoon indicating non-carcinogenic health risks.
This book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally.
Construction and Demolition Waste (C&DW) represents a significant proportion of industrial waste going to landfill. The aim of this research was to determine the current factors affecting C&DW management on construction sites in New South Wales (NSW), Australia. In order to achieve this aim, semi-structured interviews were conducted with 19 C&D practitioners and stakeholders, interview data were analysed using Nvivo software. Key factors that underlined waste management processes on construction sites were identified. These include; identifying economic values of diverted material, potential for onsite sorting, knowledge, experience and training of site operatives, a need for tenders to include accurate predictions of waste management costs and the identification of improved methods of C&DW collection and disposal. This research reveals the key factors identified by the industry specialists in NSW that would potentially hinder C&DW management targets. Accordingly, the findings are valuable to managers and environmental stakeholders concerned with C&DW management.