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Article
Publication date: 28 September 2021

Mohammed Hamza Momade, Serdar Durdyev, Dave Estrella and Syuhaida Ismail

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

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Abstract

Purpose

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

Design/methodology/approach

A 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.

Findings

The 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 implications

The 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/value

There 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.

Details

Frontiers in Engineering and Built Environment, vol. 1 no. 2
Type: Research Article
ISSN: 2634-2499

Keywords

Article
Publication date: 1 November 2023

Damir Tokic and Dave Jackson

This study is motivated in part by the fact that the unfolding 2022 bear market, which has reached the −25% drawdown, has not been preceded by the inverted 10Y-3 m spread or an…

Abstract

Purpose

This study is motivated in part by the fact that the unfolding 2022 bear market, which has reached the −25% drawdown, has not been preceded by the inverted 10Y-3 m spread or an inverted near-term forward spread.

Design/methodology/approach

The authors develop a three-factor probit model to predict/explain the deep stock market drawdowns, which the authors define as the drawdowns in excess of 20%.

Findings

The study results show that (1) the rising credit risk predicts a deep drawdown about a year in advance and (2) the monetary policy easing precedes an imminent drawdown below the 20% threshold.

Originality/value

This study three-factor probit model shows adaptability beyond the typical recessionary bear market and predicts/explains the liquidity-based selloffs, like the 2022 and possibly the 1987 deep drawdowns.

Details

Journal of Economic Studies, vol. 51 no. 5
Type: Research Article
ISSN: 0144-3585

Keywords

Article
Publication date: 27 October 2020

Deepak Trehan and Rajat Sharma

The purpose of this paper is to test relevance of the information quality (IQ) framework in understanding quality of advertisements (ads) posted by ordinary consumers.

Abstract

Purpose

The purpose of this paper is to test relevance of the information quality (IQ) framework in understanding quality of advertisements (ads) posted by ordinary consumers.

Design/methodology/approach

The main objective of this study is to assess quality ads posted on customer-to-customer (C2C) social commerce platforms from an IQ framework. The authors deployed innovative text mining techniques to generate features from the IQ framework and then used a machine learning (ML) algorithm to classify ads into three categories ‐ high quality, medium quality and low quality.

Findings

The results show that not all dimensions of IQ framework are important to assess quality of ads posted on the platforms. Potential buyers on these platforms look for appropriate amount of information, which is objective, concise and complete, to make a potential purchase decision.

Research limitations/implications

As the research focuses on specific product categories, it lacks generalisability. Therefore, it needs to be tested for other product categories.

Practical implications

The paper includes recommendation for C2C marketplaces on how to increase quality of ads posted by consumers on the platform.

Originality/value

This study has focused on the user-generated content posted by ordinary consumers on the C2C commerce platform to sell used goods. Though C2C model has been developed on ads posted on C2C platforms, it can be established for brands as it provides them with an insight into latent dimensions that a consumer shall look for in an ad on social commerce platforms.

Details

Online Information Review, vol. 45 no. 1
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 21 March 2024

Ahmad Hadipour, Zahra Mahmoudi, Saeed Manoochehri, Heshmatollah Ebrahimi-Najafabadi and Zahra Hesari

Particles are of the controlled release delivery systems. Also, topically applied olive oil has a protective effect against ultraviolet B (UVB) exposure. Due to its sensitivity to…

Abstract

Purpose

Particles are of the controlled release delivery systems. Also, topically applied olive oil has a protective effect against ultraviolet B (UVB) exposure. Due to its sensitivity to oxidation, various studies have investigated the production of olive oil particles. The purpose of this study was to use chitosan and sodium alginate as the vehicle polymers for olive oil.

Design/methodology/approach

The gelation method used to prepare the sodium alginate miliparticles containing olive oil and particles were coated with chitosan. Morphology and size, zeta potential, infrared spectrum of olive oil miliparticles, encapsulation efficiency and oil release profile were investigated. Among 12 primary fabricated formulations, formulations F5 (olive oil loaded alginate miliparticles) and F11 (olive oil loaded alginate miliparticles + chitosan coat) were selected for further evaluations.

Findings

The size of the miliparticles was in the range of 1,100–1,600 µm. Particles had a spherical appearance, and chitosan coat made a smoother surface according to the scanning electron microscopy. The zeta potential of miliparticles were −30 mV for F5 and +2.7 mV for F11. Fourier transform infrared analysis showed that there was no interaction between olive oil and other excipients. Encapsulation efficiency showed the highest value of 85% in 1:4 (olive oil:alginate solution) miliparticles in F11. Release study indicated a maximum release of 68.22% for F5 and 60.68% for F11 in 24 h (p-value < 0.016). Therefore, coating with chitosan had a marked effect on slowing the release of olive oil. These results indicated that olive oil in various amounts can be successfully encapsulated into the sodium-alginate capsules cross-linked with glutaraldehyde.

Originality/value

To the best of the authors’ knowledge, no study has used chitosan and sodium alginate as the vehicle polymers for microencapsulation of olive oil.

Details

Nutrition & Food Science , vol. 54 no. 3
Type: Research Article
ISSN: 0034-6659

Keywords

Article
Publication date: 5 December 2017

Yuliang Zhou, Mingxuan Chen, Guanglong Du, Ping Zhang and Xin Liu

The aim of this paper is to propose a grasping method based on intelligent perception for implementing a grasp task with human conduct.

Abstract

Purpose

The aim of this paper is to propose a grasping method based on intelligent perception for implementing a grasp task with human conduct.

Design/methodology/approach

First, the authors leverage Kinect to collect the environment information including both image and voice. The target object is located and segmented by gesture recognition and speech analysis and finally grasped through path teaching. To obtain the posture of the human gesture accurately, the authors use the Kalman filtering (KF) algorithm to calibrate the posture use the Gaussian mixture model (GMM) for human motion modeling, and then use Gaussian mixed regression (GMR) to predict human motion posture.

Findings

In the point-cloud information, many of which are useless, the authors combined human’s gesture to remove irrelevant objects in the environment as much as possible, which can help to reduce the computation while dividing and recognizing objects; at the same time to reduce the computation, the authors used the sampling algorithm based on the voxel grid.

Originality/value

The authors used the down-sampling algorithm, kd-tree algorithm and viewpoint feature histogram algorithm to remove the impact of unrelated objects and to get a better grasp of the state.

Details

Industrial Robot: An International Journal, vol. 45 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

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