Systematic review of application of artificial intelligence tools in architectural, engineering and construction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Hamza Momade ◽  
Serdar Durdyev ◽  
Dave Estrella ◽  
Syuhaida Ismail
Keyword(s):  
Artificial Intelligence ◽  
Structural Engineering ◽  
Construction Materials ◽  
Geographical Location ◽  
Engineering Project ◽  
Major Purpose ◽  
Content Type ◽  
Architectural Engineering ◽  
Engineering Project Management ◽  
Practical Implications

PurposeThis study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.Design/methodology/approachA thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.FindingsThe following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.Practical implicationsThe findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.Originality/valueThere are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

Welcome to the marriage of human capability and artificial intelligence

2019 ◽  
Vol 19 (1) ◽  
pp. 10-14
Author(s):  
Ryan Scott ◽  
Malcolm Le Lievre
Keyword(s):  
Artificial Intelligence ◽  
Team Building ◽  
Workplace Change ◽  
Content Type ◽  
The Future ◽  
Human Capability ◽  
Mind Set ◽  
Future Work ◽  
Crucial Part ◽  
Practical Implications

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.

Autonomous vehicles: a review of the ethical, social and economic implications of the AI revolution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shweta Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Design Methodology ◽  
Technological Advancement ◽  
Economic Implications ◽  
Content Type ◽  
The Public ◽  
Autonomous Cars ◽  
The Subject ◽  
Practical Implications

PurposeThere are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.Design/methodology/approachMaking use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.FindingsTechnology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.Research limitations/implicationsFrom robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.Practical implicationsFuture shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.Social implicationsThe study will be of interest to researchers, academics and the public in general. It will encourage further thinking.Originality/valueThis is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

How to optimize marketing strategy development

2020 ◽  
Vol 36 (8) ◽  
pp. 17-19
Keyword(s):  
Artificial Intelligence ◽  
Marketing Strategy ◽  
Design Methodology ◽  
Reading Time ◽  
Marketing Strategies ◽  
Strategy Development ◽  
Content Type ◽  
Human Creativity ◽  
Pertinent Information ◽  
Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Artificial Intelligence (AI) is enabling companies to perform many functional tasks more efficiently. Some organizations are starting to further utilize its capabilities by combining the rationality of AI with human creativity in order to optimize development of marketing strategies. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

scholarly journals Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Babak Abedin
Keyword(s):  
Artificial Intelligence ◽  
System Performance ◽  
Fuzzy Systems ◽  
Data Analytics ◽  
Design Methodology ◽  
Contingency Theory ◽  
Content Type ◽  
The Social ◽  
Opposing Effects ◽  
Practical Implications

PurposeResearch into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.Design/methodology/approachThe author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.FindingsThe author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).Research limitations/implicationsAs in other systematic literature review studies, the results are limited by the content of the selected papers.Practical implicationsThe findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.Originality/valueThis study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.

scholarly journals Robotopias: mapping utopian perspectives on new industrial technology

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rhiannon Firth ◽  
Andrew Robinson
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
New Technologies ◽  
Social Role ◽  
Content Type ◽  
Human Essence ◽  
The Relationship ◽  
Labour Power ◽  
Practical Implications

PurposeThis paper maps utopian theories of technological change. The focus is on debates surrounding emerging industrial technologies which contribute to making the relationship between humans and machines more symbiotic and entangled, such as robotics, automation and artificial intelligence. The aim is to provide a map to navigate complex debates on the potential for technology to be used for emancipatory purposes and to plot the grounds for tactical engagements.Design/methodology/approachThe paper proposes a two-way axis to map theories into to a six-category typology. Axis one contains the parameters humanist–assemblage. Humanists draw on the idea of a human essence of creative labour-power, and treat machines as alienated and exploitative form of this essence. Assemblage theorists draw on posthumanism and poststructuralism, maintaining that humans always exist within assemblages which also contain non-human forces. Axis two contains the parameters utopian/optimist; tactical/processual; and dystopian/pessimist, depending on the construed potential for using new technologies for empowering ends.FindingsThe growing social role of robots portends unknown, and maybe radical, changes, but there is no single human perspective from which this shift is conceived. Approaches cluster in six distinct sets, each with different paradigmatic assumptions.Practical implicationsMapping the categories is useful pedagogically, and makes other political interventions possible, for example interventions between groups and social movements whose practice-based ontologies differ vastly.Originality/valueBringing different approaches into contact and mapping differences in ways which make them more comparable, can help to identify the points of disagreement and the empirical or axiomatic grounds for these. It might facilitate the future identification of criteria to choose among the approaches.

The impact of artificial intelligence and machine learning on the global economy and its implication for the hospitality sector in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudhanshu Bhushan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Global Economy ◽  
Regulatory System ◽  
Content Type ◽  
Original Contribution ◽  
Hospitality Sector ◽  
Global Regulatory System ◽  
The Impact ◽  
Practical Implications

Purpose The purpose of this paper is to explore and evaluate the existing and future impact of artificial intelligence (AI) and machine learning on the global economy. It includes viewing the inclusion of AI in different sectors, its impact on industries, the trends of the forerunning companies that are capitalizing on AI and the idea of crystalizing exponential growth while maintaining a balance between the understanding of humans and the subsequent possibilities of AI. Design/methodology/approach This paper is based on secondary research, reviewing literature based on different industries and perspectives. Findings The global potential of AI is exponential; the development of AI should be effective. Globally, we see contrasting views, defining the consequences of AI. Hence, the balance between humans and AI, protocols and a global regulatory system needs to be established to prevent catastrophic results soon. Practical implications The benefits of AI are enormous. The rising incorporation of AI must take into consideration the basic safety fundamentals for a better future. Social implications This paper will enable readers to understand the importance of AI in the global economy, its current involvement in major industries and the subsequent need for balance in technology. Originality/value This conceptual review is by its nature and original contribution and, specifically, an interpretation for India.

Understanding material and supplier networks in the construction of disaster-relief shelters: the feasibility of using social network analysis as a decision-making tool

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alex Copping ◽  
Noorullah Kuchai ◽  
Laura Hattam ◽  
Natalia Paszkiewicz ◽  
Dima Albadra ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Disaster Relief ◽  
Construction Materials ◽  
Geographical Location ◽  
Original Research ◽  
Supply Network ◽  
Supply Networks ◽  
Content Type

PurposeUnderstanding the supply network of construction materials used to construct shelters in refugee camps, or during the reconstruction of communities, is important as it can reveal the intricate links between different stakeholders and the volumes and speeds of material flows to the end-user. Using social network analysis (SNA) enables another dimension to be analysed – the role of commonalities. This is likely to be particularly important when attempting to replace vernacular materials with higher-performing alternatives or when encouraging the use of non-vernacular methods. This paper aims to analyse the supply networks of four different disaster-relief situations.Design/methodology/approachData were collected from interviews with 272 displaced (or formally displaced) families in Afghanistan, Bangladesh, Nepal and Turkey, often in difficult conditions.FindingsThe results show that the form of the supply networks was highly influenced by the nature/cause of the initial displacement, the geographical location, the local availability of materials and the degree of support/advice given by aid agencies and or governments. In addition, it was found that SNA could be used to indicate which strategies might work in a particular context and which might not, thereby potentially speeding up the delivery of novel solutions.Research limitations/implicationsThis study represents the first attempt in theorising and empirically investigating supply networks using SNA in a post-disaster reconstruction context. It is suggested that future studies might map the up-stream supply chain to include manufacturers and higher-order, out of country, suppliers. This would provide a complete picture of the origins of all materials and components in the supply network.Originality/valueThis is original research, and it aims to produce new knowledge.

The impacts of artificial intelligence (AI) on jobs: an industry perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Placide Poba-Nzaou ◽  
Malatsi Galani ◽  
Sylvestre Uwizeyemungu ◽  
Arnela Ceric
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
Employability Skills ◽  
Rapid Review ◽  
Professional Literature ◽  
Content Type ◽  
Job Tasks ◽  
Hr Function ◽  
Near Future ◽  
Practical Implications

Purpose This paper aims to explore the impacts of artificial intelligence (AI) on jobs. Design/methodology/approach The authors followed rapid review guidelines. The authors collected industry and government reports published prior and up to August 2017 in Google and Google Scholar using combination of key words: “job automation” or “work automation” with technology keywords: “artificial intelligence,” “machine learning,” etc. In total, 11 were included in this research. Findings The use of AI technologies will impact jobs in the near future as some job tasks are automated. AI is likely to substitute both, routine and nonroutine tasks. It is expected that humans and robots would work together in ways never imaginable. Changes in employability skills are expected. Because of the magnitude of these impacts on jobs, consulted reports call for concerted solutions that go beyond organizations’ and industry’s boundaries to include other relevant stakeholders. Moreover, organizations will have to rethink their human resource (HR) function to realign its expertise to the reality of AI. Practical implications In this context, the HR function will have to understand the dynamics that generate the impacts of these technologies in a workplace, to anticipate changes and actively contribute to creating an organizational environment that will facilitate the collaboration between human workers and complex digital agents, while ensuring compliance with labor and employment laws and supporting strategic organizational objectives. Originality/value This paper contributes to the debate on ongoing concerns by providing a synthesis of relevant professional literature.

Artificial intelligence-based training learning from application

2019 ◽  
Vol 33 (2) ◽  
pp. 20-23 ◽  
Author(s):  
Ashwani Kumar Upadhyay ◽  
Komal Khandelwal
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
Theoretical Foundation ◽  
Relevant Information ◽  
Relevant Research ◽  
Research Papers ◽  
Content Type ◽  
Training Systems ◽  
Effectiveness Of Training ◽  
Practical Implications

Purpose This paper aims to discuss the rationale, theoretical foundation, application, and future of artificial intelligence (AI)-based training. Design/methodology/approach A review of relevant research papers, articles and case studies is done to highlight developments in research and practice. Findings AI-based training systems are smart, intelligent and expert in handling queries. These systems can curate content, grade, evaluate, and provide feedback to trainee, thus making learning adaptive and contextual. Practical implications Application of AI is vital in the field of training, as it helps personalization and customization of training programs to increase the effectiveness of training. Originality/value Executives and researchers can save time by reading relevant information on the linkage, and its contribution to AI is discussed and summarized in an easy to read format.

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