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Record W6957856549 · doi:10.6084/m9.figshare.21641672

Adoption of Artificial Intelligence for Optimum Productivity in the Construction Industry

2022· dissertation· en· W6957856549 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2022
Typedissertation
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsConstruct (python library)Process (computing)ProductivityWork (physics)Plan (archaeology)VisibilityConstruction industry

Abstract

fetched live from OpenAlex

While the building business has come a long way since its inception, the technology necessary to reshape it has yet to find a home. The digital switch has now made its way into the construction sector, intending to increase productivity. Artificial intelligence (AI) is a branch of computer science defined as a machine's capacity to imitate intelligent human behaviour by simulating traditionally complex issues using human-inspired methods. Due to the intricacy of AI, it stands apart from lesser levels of digitalisation. The complexity of AI necessitates the establishment of new conditions for human trust and cooperation. This thesis is a pioneering work in that it examines the implementation of AI and the appropriate interaction between people and AI-based technology.This thesis aims to shed light on how the construction sector may narrow the gap between artificial intelligence deployment's potential and realised advantages. The gap was discovered by comparing the potential advantages of AI implementation to the current benefits and obstacles to AI implementation in the construction sector.This thesis presents research based on a comprehensive literature review, case studies of Speller Metcalfe, a designbuild and refurbishment project in Malvern, England, Jacobsen Construction, a project digitising the planning process in Salt Lake City, Utah, USA, Using a CISCO packet tracer simulator to construct a smart house in Calgary city Canada and Menkes Development Inc., real-time visibility to construction site insights and data-driven decision-making in Toronto, Canada.This research indicates that AI's capacity to evaluate millions of datasets, constantly learn from the data produced, and act on statistics may result in several potential advantages for the construction sector. AI is here to stay, and when used well, it may result in improved production, increased safety, and higherquality building.The construction sector is only getting started with AI-based technology deployment. However, this research demonstrates that knowledge gained via the implementation of fundamental digital technology may be used to develop advanced technologies, such as artificial intelligence. User-friendly tools, a well-defined training plan, a desire and incentive to learn, and trust and respect amongst contractors are critical elements in successfully adopting basic digital technology. They may also be considered essential when implementing AI-based technology.A plan for amassing an adequate quantity of high-quality data must be devised to narrow the gap between the future and present state of affairs. However, it is determined that the most critical element in reaping the advantages of AI-based technology is trust between humans and machines. The following are essential elements for establishing human-AI trust: transparent AI systems, human-AI contact, education, time, and experience. Additional research should be conducted on international initiatives and other sectors to gain knowledge from their experiences. Additionally, it is suggested to track the deployment of AI in different case studies and see how it is carried out in reality. This effort should include determining a data collection strategy and determining the degree of transparency and interactivity required in the AI system to achieve a suitable level of human-AI trust.Artificial intelligence is a rapidly growing area with applications in virtually every industry; its uses have improved workplace productivity. However, the construction sector has been sluggish to adapt to the digital age, with businesses failing to use and embrace new technology.Artificial intelligence is advancing at a breakneck pace, changing our society and opening up unprecedented possibilities in a wide variety of industries, including the construction industry. Recent technology advancements in artificial intelligence, automation, and robotics significantly impact the construction sector. Artificial intelligence is advancing quicker than ever before in a wide variety of areas throughout the technological age. New technology creates exciting possibilities without question, but it also introduces significant uncertainties and difficulties in legal measurements. It is critical to understand the legal risks and problems surrounding using artificial intelligence in different sectors to make educated choices. Regulators must pay urgent attention to artificial intelligence because of its difficulties with current legal frameworks and the new legal and ethical issues it raises. This article addresses significant regulatory problems in Artificial Intelligence in the building industry: how to stay up with technical advancements while maintaining a balance between innovation and individual rights protection. The legal implications of using artificial intelligence (AI) and self-driving cars on construction sites should be thoroughly considered. There is a shortage of laws regulating the usage and development of AI and autonomous vehicles in the construction industry. All of these problems and legal difficulties need regulators' and legislators' attention and must be handled. Systemes d'Augmentation de l'intelligence. Construction projects are one-of-a-kind and often massive, complicated, and safety essential, making it challenging to implement change and depending on established procedures. The procedures used to design construction projects include diverse expertise areas and many stakeholders who must all work together to ensure the project's success.Artificial intelligence is a fascinating technical development that has the potential to enhance building project management significantly. However, despite these potential advantages, AI has received little attention due to various reasons, including high implementation costs, data preparation requirements, a lack of AI methods, and a shortage of qualified people.This study is groundbreaking in this respect. It evaluates how artificial intelligence solutions and methods may be utilised to assist project managers and other relevant professionals in effectively planning and managing construction projects. A mixed-method research design was used to collect quantitative and qualitative data via structured online survey questionnaires to accomplish this goal. This approach provides an in-depth understanding of the topic while also identifying the challenges professionals and organisations face, followed by recommended steps for implementing AI in their workflow.The study effectively elicited a better knowledge of the requirements of construction project professionals and organisations and in creating a framework for AI specialists to utilise in building future AI solutions for construction project planning.The rapidly evolving collection of artificial intelligence (AI) technologies has the potential to address some of Sub-Saharan Africa's most urgent problems and to propel growth and development in key sectors: • Agriculture will be more productive and efficient, resulting in increased yields. • Healthcare will become more personalised, outstanding quality, and accessible, resulting in improved results. • Public services will improve their efficiency and responsiveness to people, thus increasing their effect. • Financial services will become more secure and accessible to a more significant number of people in need. Forward-thinking policymakers, creative entrepreneurs, global technology partners, civil society organisations, and international stakeholders are already mobilising to support the development of a thriving AI ecosystem in Africa. However, structural obstacles persist that may stymie Africa's establishment of a robust AI ecosystem: • Education systems will need to change rapidly, and new frameworks for employees and people to acquire the skills necessary for survival will need to be developed. • Broadband coverage must be quickly expanded — particularly in rural regions — to ensure that all people and businesses benefit. • Ethical concerns associated with the fair, secure, and inclusive usage of AI applications must also be addressed via cooperation and participation for AI systems to gain confidence. • Ensuring a larger, more diverse, and more accessible data pool is also critical for academics, developers, and users to advance AI. As is the case with previous transformational and revolutionary technologies, AI development is fraught with difficulties. Governments may overcome these obstacles and reap the benefits of artificial intelligence by developing clear roadmaps for the technology's deployment. They should realign their laws and legal frameworks to promote data-driven technologies and innovation-driven growth; improve the development infrastructure; and establish the tone for a collaborative approach that encourages all stakeholders to contribute their knowledge, ideas and create trust. Africa and its people may enjoy the advantages of changes in the years to come with the proper combination of policies.The construction industry's development is severely constrained by a slew of complicated problems, including cost and schedule overruns, health and safety, productivity, and labour shortages. Additionally, the construction sector is one of the least digitalized globally, making it challenging to address the issues it is presently facing. Artificial Intelligence (AI), a cutting-edge digital technology, is currently reshaping the manufacturing, retail, and telecommunications sectors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.005
Scholarly communication0.0090.006
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.275
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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