Trustworthy AI and robotics: Implications for the AEC industry

Emaminejad, Newsha; Akhavian, Reza

doi:10.1016/j.autcon.2022.104298

Cited by 63 publications

(26 citation statements)

References 93 publications

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“…AI can be used as an important tool in the experimental design that can generate suitable variables for the modeling as well as optimization of pollutants removal efficiency [20,21]. AI has achieved a boundless establish in various areas [22] such as big data [23], pattern recognition [24], image understanding [25], robotics [26], intelligent search engines [27], automatic programming [28], autonomous driving [29,30] and human-computer games [31]. AI technology manages the design of cyber security systems [32,33] and programs that are adequate to imitate human features such as problem-solving, learning, perception, reasoning, and experiences from surroundings [34,35].…”

Section: Discussionmentioning

confidence: 99%

A Systematic Survey on the Research of AI-predictive Models for Wastewater Treatment Processes

2023

ijcsm

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Context: To increase the efficiency of wastewater treatment, modeling and optimization of pollutant removal processes are the best solutions. The relationship between input and output parameters in wastewater treatment processes (WWTP) are the complicated one, and it is difficult for designing of models using statistics. Artificial Intelligence (AI) models are generally more flexible when comparing with statistical models while modeling complex datasets with nonlinearity and missing data. Objective: Studies on WWTP of AI based are increasing day by day. Therefore, it is crucial to systematically review the AI techniques available which are implemented for WWTP. Such kind of review helps for classifying the techniques that are invented and helps to identify challenges as well as gaps for future studies. Lastly, can sort out the best AI technique to design predictive models for WWTP. Method: With the help of the most relevant digital libraries, the total number of papers collected are 1222 which are based on AI modelling on WWTP. Then the filtration of the papers is mainly based on the inclusion and exclusion criteria. Also, to identify new relevant papers, snowballing is the other technique applied. Results: Finally selected 76 primary papers to reach the result that were published between 2004 and 2020. Conclusion: ANN with MLP approach on BP algorithm become a supervised neural network called BPNN is the most used AI modelling for WWTP and around 40% of the experimental research done with BPNN. Then there are some limitations on AI modelling of WWTP using photoreforming which is the current study of WWTP represents a promising path for generating renewable and sustainable energy resources like chemicals and fuels.

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Section: Discussionmentioning

confidence: 99%

A Systematic Survey on the Research of AI-predictive Models for Wastewater Treatment Processes

2023

ijcsm

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“…Well-founded knowledge about motives for action, needs and their critical reflections helped to develop constructive approaches. Literature sources in other disciplines, that deal with similar research questions, proved to be particularly informative for the Construction Industry [27][28][29] . Here, the study aims to anchor the Construction Industry in the interdisciplinary scientific debate led by trustworthy AI.…”

Section: Methodsmentioning

confidence: 99%

Best Practices in Construction 4.0 – Catalysts of digital innovations (Part I)

Weber-Lewerenz

Traverso

2023

j. of archit. environ. & struct. eng. res.

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Digital transformation in the AEC industry (Architecture, Engineering and Construction) is a key driver to enhance technical innovation in the branch and adds dynamic to all work processes and methods. A more differentiated understanding of the responsible use of innovative technologies aims not only towards increased sustainability and more efficient building life cycles but also recognizing the unintended effects such as artificial intelligence (AI). The study is part of a larger primary research on Corporate Digital Responsibility (CDR) in Construction 4.0; this identifies, analyzes and systematically evaluates key factors of a sustainable digital transformation, especially in the traditionally small-scale Construction Industry - one in which there can be no standardized procedure. The study uses interdisciplinary literature and data research and expert interviews. The qualitative method enables a critical-reflexive analysis of the key factors of a meaningful and sustainable implementation of innovative technologies in Construction. Application examples show possible approaches - some of which are implemented as prototypes - and provide guidance for small to medium-sized companies. The study outlines the necessary steps for companies to define their own potential fields of application and find suitable methods. Another aim of the study is to take stock of the acceptance of new technologies by comparing different perspectives from experts. The study results show new perspectives in the transformation of the Construction Industry. They show that Digital Transformation in Construction 4.0 has great potential for an economical, efficient construction life cycle, but requires the responsible, sensible use of innovative technologies.

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“…Already today, many AI examples can be found, in which robots are used on construction sites. [33][34][35] However, widespread application of digital or intelligent machines has only been observed in some occasional projects. Even though an adequate number of technologies are already very advanced, many of them lack practical realization; the existing approaches are often in early stages of only a few research projects.…”

Section: Applications In Aec: Construction Robots and Other Examplesmentioning

confidence: 99%

“…Already today, many AI examples can be found, in which robots are used on construction sites 33–35 . However, widespread application of digital or intelligent machines has only been observed in some occasional projects.…”

Section: Applications In Aec: Construction Robots and Other Examplesmentioning

confidence: 99%

Self‐organizing systems in the construction industry

Lohmann

Kölzer

Schaan

et al. 2023

Engineering Reports

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Self‐organization is found in numerous natural and artificial systems. For the construction industry, self‐organizing processes are not only applicable in managing or organizing tasks, they also provide insight into materials or can be implemented into operations on building sites. In this paper, the terms feedback, agents, emergence and self‐organization are described to provide an insight into the complex processes relevant to a lot of fields in the construction industry. For this, the principles of adaptive processes are transferred to artificial systems. Besides self‐organization in building materials, an outlook is given to describe construction sites as cyber‐physical systems. Inside such systems, agents learn to organize work tasks more efficiently, particularly with the help of reinforcement learning. In this regard, a transfer of natural self‐organization—which also exist in materials—to building processes is made. A combination of natural and artificial systems is presented to promote self‐organization for various fields of application in the construction industry, especially for building sites.

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Trustworthy AI and robotics: Implications for the AEC industry

Cited by 63 publications

References 93 publications

A Systematic Survey on the Research of AI-predictive Models for Wastewater Treatment Processes

A Systematic Survey on the Research of AI-predictive Models for Wastewater Treatment Processes

Best Practices in Construction 4.0 – Catalysts of digital innovations (Part I)

Self‐organizing systems in the construction industry

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