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This study establishes an ontology-based framework for rework risk identification (RRI) by integrating heterogeneous data from the information flow of the prefabricated construction (PC) process. The main objective is to enhance the automation level of rework management and reduce the degree of reliance on human factors and manual operations.

The proposed framework comprises four levels aimed at managing dispersed rework risk knowledge and integrating heterogeneous data. The functionalities were realised through an integrated ontology that aligned the rework risk ontology with the PC ontology. The ontologies were developed and edited with Protégé. Ultimately, the potential benefit of the framework was validated through a case study and an expert questionnaire survey.

The framework is proven to effectively manage rework risk knowledge and can identify risk objects, clarify risk factors, determine risk events, and retrieve risk measures, thereby enabling the pre-identification of prefabricated rework risk (PRR) and improving the automation level. This study is meaningful and lays the foundation for the application of other computer methods in rework management research and practice in the future.

This research provides insights into the application of ontology to solve rework risk issues in the PC process and introduces a novel risk management method for future prefabricated project research and practice. The findings have significant theoretical value in terms of enriching the methods of risk assessment and control and the information management system of prefabricated projects.

This paper aims to optimize cable-stayed force in asymmetric one-tower cable-stayed bridge formation using an improved particle swarm algorithm. It compares results with the traditional unconstrained minimum bending energy method.

This paper proposes an improved particle swarm algorithm to optimize cable-stayed force in bridge formation. It formulates a quadratic programming mathematical model considering the sum of bending energies of the main girder and bridge tower as the objective function. Constraints include displacements, stresses, cable-stayed force, and uniformity. The algorithm is applied to optimize the formation of an asymmetrical single-tower cable-stayed bridge, combining it with the finite element method.

The study’s findings reveal significant improvements over the minimum bending energy method. Results show that the structural displacement and internal force are within constraints, the maximum bending moment of the main girder decreases, resulting in smoother linear shape and more even internal force distribution. Additionally, the tower top offset decreases, and the bending moment change at the tower-beam junction is reduced. Moreover, diagonal cable force and cable force increase uniformly with cable length growth.

The improved particle swarm algorithm offers simplicity, effectiveness, and practicality in optimizing bridge-forming cable-staying force. It eliminates the need for arbitrary manual cable adjustments seen in traditional methods and effectively addresses the optimization challenge in asymmetric cable-stayed bridges.

Building on social exchange theory (SET), the main aim of this paper is to empirically study the impact of high-commitment work practices (HCWPs) systems on radical innovation. Additionally, the paper examines the mediating roles of employee innovative work behaviour (IWB) and knowledge sharing (KS) in the relationship between HCWPs and radical innovation.

Using a survey questionnaire, data were collected from employees working in pharmaceutical, manufacturing and technological industries in Jordan. A total of 408 employees participated in the study. Structural equation modelling (SEM) using AMOS v28 was employed to test the research hypotheses.

This research found that HCWPs in the form of a bundle of human resource management (HRM) practices are significant for employee IWB and KS. However, similar to previous studies, this paper failed to find a direct significant impact for HCWPs on radical innovation. Rather, the impact was mediated by employee IWB. Additionally, this paper found that HCWPs are significant for KS and that KS is significant for employee IWB.

Distinctively, this paper considered the mediating effect of employee IWB on radical innovation. Extant research treated IWB as a consequence of organisational arrangements such as HRM practices; this paper considered IWB as a foundation and source for other significant organisational outcomes, namely radical innovation. Additionally, the paper considered employees' perspectives in studying the relationship between HRM, KS, IWB and radical innovation.

This paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.

The adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.

This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.

The combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.

The novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

As an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.

First of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.

The feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.

This paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.

This research explores the role of concrete 3D printing (C3DP) in the development of culturally appropriate housing in Indigenous Reserves in Canada through the design, building and evaluation of the Star Lodge project located in the Siksika Nation of Alberta, Canada. The project aims to assess the potential of C3DP in addressing the escalating housing demands in Indigenous communities in Canada.

The research involved a collaborative and multidisciplinary approach, engaging Blackfoot Elders, Knowledge Keepers from the Siksika Nation, Siksika Housing and Nidus3D. Central to this was the design, build and documentation of the Star Lodge project to analyse the advantages and challenges, guided by weekly meetings and site visits.

The project harnessed C3DP to streamline construction, enhance durability, reduce maintenance costs and enhance the energy performance of the homes. Notable time savings were achieved compared to conventional construction methods. Challenges included developing strategies to overcome extreme cold weather conditions, achieving a consistent concrete mix and integrating conventional construction elements such as drywall construction in interiors. The project served as a platform for collaboration and community participation, shaping the design and construction process while raising awareness of innovative construction techniques in the community.

This study provides an evidence-based framework for the evaluation of C3DP technology by analysing the Star Lodge Project, the first C3DP project in Alberta and the largest of its kind in Canada. By addressing housing challenges in Indigenous communities, the research holds broader implications for sustainable development and Indigenous empowerment across Canada.

This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

In order to improve the computation efficiency of the four-point rainflow algorithm, a new fast four-point rainflow cycle counting algorithm (FFRA) using a novel loop iteration mode is proposed.

In this new algorithm, the loop iteration mode is simplified by reducing the number of iterations, tests and deletions. The high efficiency of the new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems.

The extensive simulation results show that the extracted cycles by the new FFRA are the same as those by the four-point rainflow cycle counting algorithm (FRA) and the three-point rainflow cycle counting algorithm (TRA). Especially, the simulation results indicate that the computation efficiency of the FFRA has improved an average of 12.4 times compared to the FRA and an average of 8.9 times compared to the TRA. Moreover, the equivalence of cycle extraction results between the FFRA and the FRA is proved mathematically by utilizing some fundamental properties of the rainflow algorithm. Theoretical proof of the efficiency improvement of the FFRA in comparison to the FRA is also given.

This merit makes the FFRA preferable in online monitoring systems of structures where fatigue life estimation needs to be accomplished online based on massive measured data. It is noticeable that the high efficiency of the FFRA attributed to the simple loop iteration, which provides beneficial guidance to improve the efficiency of existing algorithms.

The purpose of this study was to analyze, through a sequential model, the underlying mechanisms connecting transformational leadership with employee task performance. Specifically, it examined the causal chain of transformational leadership-job characteristics (task variety and task significance)-job engagement-task performance.

The hypotheses were tested on a sample of 320 employees and their supervisors from Spanish young technology and knowledge-intensive small and medium-sized enterprises (SMEs) using structural equations.

The results show that the effect of transformational leadership on task performance (assessed by supervisors) occurs sequentially via task significance and job engagement. Additionally, job engagement mediates the relationship between task variety and task performance.

Findings highlight the relevance for supervisors to employ a transformational leadership style that leads employees directly and indirectly, through task significance, to be more engaged and achieve higher task performance. They also emphasize the importance of proper job design that allows employees to be fully invested in their job performance.

Despite the importance of leadership and employee performance for the survival and growth of these firms, the study of these relationships is largely unexplored. This study proposes and tests a serial model in which supervisor transformational leadership is linked to employee task performance through two sequential mediators: job characteristics (task variety and task significance) and job engagement.

The “chiralisation” of Euclidean polygonal tessellations is a novel, recent method which has been used to design new auxetic metamaterials with complex topologies and improved geometric versatility over traditional chiral honeycombs. This paper aims to design and manufacture chiral honeycombs representative of four distinct classes of 2D Euclidean tessellations with hexagonal rotational symmetry using fused-deposition additive manufacturing and experimentally analysed the mechanical properties and failure modes of these metamaterials.

Finite Element simulations were also used to study the high-strain compressive performance of these systems under both periodic boundary conditions and realistic, finite conditions. Experimental uniaxial compressive loading tests were applied to additively manufactured prototypes and digital image correlation was used to measure the Poisson’s ratio and analyse the deformation behaviour of these systems.

The results obtained demonstrate that these systems have the ability to exhibit a wide range of Poisson’s ratios (positive, quasi-zero and negative values) and stiffnesses as well as unusual failure modes characterised by a sequential layer-by-layer collapse of specific, non-adjacent ligaments. These findings provide useful insights on the mechanical properties and deformation behaviours of this new class of metamaterials and indicate that these chiral honeycombs could potentially possess anomalous characteristics which are not commonly found in traditional chiral metamaterials based on regular monohedral tilings.

To the best of the authors’ knowledge, the authors have analysed for the first time the high strain behaviour and failure modes of chiral metamaterials based on Euclidean multi-polygonal tessellations.