Ahmad Honarjoo, Ehsan Darvishan, Hassan Rezazadeh and Amir Homayoon Kosarieh
This article addresses the need for a comprehensive model for structural crack detection in the context of structural health monitoring. The main innovation of this research is…
Abstract
Purpose
This article addresses the need for a comprehensive model for structural crack detection in the context of structural health monitoring. The main innovation of this research is the introduction of a dynamic attention-based transformer model that significantly enhances the accuracy and efficiency of detecting and localizing cracks in structures. This study seeks to overcome previous limitations and contribute to advancements in structural health monitoring practices.
Design/methodology/approach
The research focuses on three primary computer vision tasks: classification, object detection and semantic segmentation applied to crack detection in concrete, brick and asphalt structures. The proposed approach employs transformer encoders with dynamic attention mechanisms to assess the severity and extent of damage accurately.
Findings
In this study, we propose a dynamic attention-based transformer model for structural crack detection, achieving a remarkable accuracy of 99.38% and an impressive F1 score. Our method demonstrates superior performance compared to existing techniques, such as the fusion features-based broad learning system and deep convolutional neural networks, while also significantly reducing execution time, highlighting its efficiency and potential for practical applications in structural health monitoring.
Originality/value
This research introduces a novel framework for crack detection, leveraging recent advancements in deep learning technology, with significant implications for the field of civil engineering and maintenance.
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Ahmad Honarjoo, Ehsan Darvishan, Hassan Rezazadeh and Amir Homayoon Kosarieh
This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact…
Abstract
Purpose
This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.
Design/methodology/approach
SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.
Findings
The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.
Originality/value
SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.
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Tahereh Karimi, Zeinab Moslemi, Arezoo Rezazadeh and Hassan Eini-Zinab
This study aims to examine the effect of maternal food intake before and during pregnancy on birth weight.
Abstract
Purpose
This study aims to examine the effect of maternal food intake before and during pregnancy on birth weight.
Design/methodology/approach
As a prospective cohort study, a total of 585 pregnant women of first trimester, visiting Tehran Metropolitan Area public health centers and private sectors (clinics and hospitals), were interviewed at first phase, and pregestational dietary intake was obtained by a 168-item semiquantitative food frequency questionnaire. At the third trimester, dietary recalls were collected via interview. Finally, birth weight information was extracted from health records. Univariate and multivariate analysis was used to explore the effect of maternal and nutritional factors on birth weight.
Findings
The results of the analysis show that direct measures of nutrition, measured as food group consumption at first and third trimester of pregnancy, had no significant effect on birth weight once the confounding factors were controlled (p > 0.05). Of control variables included in the analysis, twin pregnancy outcome (p = 0.000), pregnancy number (p = 0.04), prepregnancy weight (p = 0.004) (marginally significant) and gestational age (p = 0.000) (marginally significant) were associated with birth weight.
Originality/value
The results of this study show no significant role of mother’s nutrition during pregnancy on birth weight, while long-term nutrition outcomes such as prepregnancy weight had significant role. It seems the main reasons behind less important role of pregnancy nutrition on birth weight in this study include the following: food intake deficiency is not a major problem for participants, and cross-sectional data on food intake are less important on outcome of pregnancy weight than long-term nutritional status outcome variables such as mother’s weight and height. This finding should be addressed in public health planning for women at childbearing age.
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Amr A. Mohy, Hesham A. Bassioni, Elbadr O. Elgendi and Tarek M. Hassan
The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) techniques for improving safety on construction sites. It provides an…
Abstract
Purpose
The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) techniques for improving safety on construction sites. It provides an overview of the current state of research in the field of construction site safety (CSS) management using these technologies. Specifically, the study focuses on identifying hazards and monitoring the usage of personal protective equipment (PPE) on construction sites. The findings highlight the potential of computer vision and DL to enhance safety management in the construction industry.
Design/methodology/approach
The study involves a scientometric analysis of the current direction for using computer vision and DL for CSS management. The analysis reviews relevant studies, their methods, results and limitations, providing insights into the state of research in this area.
Findings
The study finds that computer vision and DL techniques can be effective for enhancing safety management in the construction industry. The potential of these technologies is specifically highlighted for identifying hazards and monitoring PPE usage on construction sites. The findings suggest that the use of these technologies can significantly reduce accidents and injuries on construction sites.
Originality/value
This study provides valuable insights into the potential of computer vision and DL techniques for improving safety management in the construction industry. The findings can help construction companies adopt innovative technologies to reduce the number of accidents and injuries on construction sites. The study also identifies areas for future research in this field, highlighting the need for further investigation into the use of these technologies for CSS management.
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Tooraj Karimi, Mohammad Reza Sadeghi Moghadam and Amirhosein Mardani
This paper aims to design an expert system that gets data from researchers and determines their maturity level. This system can be used for determining researchers’ support…
Abstract
Purpose
This paper aims to design an expert system that gets data from researchers and determines their maturity level. This system can be used for determining researchers’ support programs as well as a tool for researchers in research-based organizations.
Design/methodology/approach
This study focuses on designing the inference engine as a component of an expert system. To do so, rough set theory is used to design rule models. Various complete, discretizing and reduction algorithms are used in this paper, and different models were run.
Findings
The proposed inference engine has the validity of 99.8 per cent, and the most important attributes to determine the maturity level of researchers in this model are “commitment to research” and “attention to research plan timeline”.
Research limitations/implications
To accurately determine researchers’ maturity model, solely referring to documents and self-reports may reduce the validation. More validation could be reached through using assessment centers for determining capabilities of samples and observations in each maturity level.
Originality/value
The assessment system for the professional maturity of researchers is an appropriate tool for funders to support researchers. This system helps the funders to rank, validate and direct researchers. Furthermore, it is a valid criterion for researchers to evaluate and improve their abilities. There is not any expert system to assess the researches in literature, and all models, frameworks and software are conceptual or self-assessment.
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Tooraj Karimi and Arvin Hojati
In this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used…
Abstract
Purpose
In this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.
Design/methodology/approach
Some tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.
Findings
In this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.
Research limitations/implications
The total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.
Originality/value
The main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.
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Hong Zhang and Lu Yu
Prefabricated construction concerns off-site production, multi-mode transportation and on-site installation of the prefabricated components, which are interdependent and…
Abstract
Purpose
Prefabricated construction concerns off-site production, multi-mode transportation and on-site installation of the prefabricated components, which are interdependent and dynamically interactive, so coordination among the multiple stages along the prefabricated component supply chain (PCSC) is indispensable. This study aims to solve the dynamic transportation planning problem for the PCSC by addressing the interdependency, dynamic interaction and coordination among the multiple stages and different objectives of the stakeholders.
Design/methodology/approach
The PCSC is analyzed and then the formulation for the dynamic transportation planning problem is developed based on the just-in-time (JIT) strategy. The particle swarm optimization (PSO) algorithm is applied to solve the dynamic optimization problem.
Findings
The proposed dynamic transportation planning method for the PCSC regarding component supplier selection, transportation planning for means, routes and schedule, site layout planning and transportation plan adjustment is able to facilitate coordination among the multiple stages by addressing their interdependencies and dynamic interactions, as well as different economic objectives of the stakeholders such as suppliers or the contractor.
Originality/value
The study helps to achieve the advantages of prefabricated construction by prompting coordination among multiple stages of the PCSC by realizing different benefits of the stakeholders. In addition, it provides the stakeholders with the competitive bidding prices and the evaluation data for the bids quote. Meanwhile, it contributes to the domain knowledge of the PCSC management with regard to the viewpoint of coordination and integration of multiple stages rather than only one stage as well as the dynamic optimization model based on the JIT strategy and the PSO algorithm.
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Stavros Sindakis, Sakshi Aggarwal and Panagiotis Theodorou
The chapter reflects on the coopetition concept and its role in combining cooperation and competition with a paradoxical situation between them. Likewise, it illustrates how…
Abstract
The chapter reflects on the coopetition concept and its role in combining cooperation and competition with a paradoxical situation between them. Likewise, it illustrates how coopetition gains a mutual advantage for the collaborative relationship of value creation, new product development (NPD), knowledge acquisition, and organizational performance in inter-organizations. However, it is necessary to build a friendly approach and proper management to ensure effective coopetition. The rationale, though, is the following: coopetition defines innovation performance; coopetition represents knowledge recombination in both inter-organizational and intra-organizational conditions; the coopetition outcomes state knowledge creation and firm’s innovation that lead to new ideas and new variations improving organizational relationships. At the same time, it is highlighted how the customer relationships are aligned in the enterprises and how knowledge transfer in different alliances influence dynamic and complex character exploring tacit and explicit knowledge. As far as knowledge is concerned in an organization, it illustrates the contribution of two essential elements in coopetition value creation, i.e., knowledge management and intellectual capital. By critically evaluating the following, we encourage coopetition and innovation to recognize knowledge and increase the performance of inter-organizational units. Moreover, the primary way for knowledge assessment in organizations is by collaboration with the competitors. Concluding, our theoretical approach acknowledges that knowledge sharing enables more efficient innovation by linking R&D efforts.
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Hongyun Tian, Shuja Iqbal and Shamim Akhtar
In the competitive business world, companies strive to be innovative, and to do so, they try to implement innovative human resource practices. Therefore, the authors propose an…
Abstract
Purpose
In the competitive business world, companies strive to be innovative, and to do so, they try to implement innovative human resource practices. Therefore, the authors propose an association between innovative human resource practice, organizational commitment, innovation performance and transformational leadership.
Design/methodology/approach
This study gathered data from 1,037 small- and medium-sized enterprises and implied partial least square structural equation modeling PLS-SEM using Smart PLS was adopted to test the hypotheses.
Findings
The findings reveal positive direct relationships between innovative human resource practices, organizational commitment and innovation performance. Moreover, organizational commitment positively mediates and transformational leadership significantly and positively moderates the relationship. Companies should use innovative recruitment and selection, performance management, and innovative compensation to enhance organizational commitment and innovation performance. In addition, the optimized organizational commitment aids in strengthening the connection between innovative human resource practices and firms' innovation performance.
Originality/value
Managers should also develop a sense of affiliation and attachment to increase innovation performance. The study contributes empirically to the literature on innovative human resource practices and their effect on organizational commitment and innovation performance.
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Dmaithan Abdelkarim Almajali, Tha’er Majali, Ra'ed Masa'deh, Mohmood Ghaleb Al-Bashayreh and Ahmad Mousa Altamimi
The commonly used e-procurement systems still show unsatisfactory implementation outcomes because many organisations are still unable to effectively interpret the initial adoption…
Abstract
Purpose
The commonly used e-procurement systems still show unsatisfactory implementation outcomes because many organisations are still unable to effectively interpret the initial adoption decision. The e-procurement systems are generally developed at organisational level, but their usage is at individual level, by the employees particularly. This paper examined technology acceptance model’s (TAM) key antecedents, involving e-procurement systems usage by employees in their daily activities. This study aims to examine the impact of factors affecting e-procurement acceptance among users through the mediating role of users’ attitude. The commonly used e-procurement systems still show.
Design/methodology/approach
TAM was applied and expanded in this study, in exploring the factors impacting the employees’ e-procurement acceptance. This study used quantitative method, and questionnaires were distributed to 200 users in Jordanian public shareholding firms. The collected data were quantitatively analysed using PLS modelling.
Findings
Significant TAM relationships involving e-procurement were affirmed. The expanded TAM in the scrutiny of antecedents showed that content, processing and usability affected perceived usefulness, while perceived convenience did not affect the usefulness factor. Furthermore, it was noticed that perceived ease of use was affected by usability and training, while perceived connectedness was not affected by usability and training.
Practical implications
The results demonstrated the necessity of e-procurement training. Furthermore, at the start of the implementation stage, effective design on system navigation and system usability, and consistent support, could increase use effectiveness and acceptance. Also, expedient information and buyer–supplier product flows should be made available.
Originality/value
Most organizations invest a lot of time and money in installing e-procurement systems to deliver their goods at the right time and at the right price. However, many of these e-procurement systems have failed due to low acceptance among users. Thus, to the best of the authors’ knowledge, this is the first study that used TAM and theory of planned behaviour in examining the effects of perceived convenience, perceived connectedness, content, training, processing and usability factors, in Jordanian firms. Lastly, the focus of this study was on the individual employee’s acceptance, rather than on the organisational-level adoption, as the unit of analysis, to provide insight on how organisations can achieve maximally from e-procurement investments and from other comparable technologies of e-supply chain management.