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Simulations exist for the prediction of the behaviour of building structural systems under fire, including two-way coupled fire-structure interaction. However, these simulations do not include detailed models of the connections, whereas these connections may impact the overall behaviour of the structure. Therefore, this paper proposes a two-scale method to include screw connections.

The two-scale method consists of (a) a global-scale model that models the overall structural system and (b) a small-scale model to describe a screw connection. Components in the global-scale model are connected by a spring element instead of a modelled screw, and the stiffness of this spring element is predicted by the small-scale model, updated at each load step. For computational efficiency, the small-scale model uses a proprietary technique to model the behaviour of the threads, verified by simulations that model the complete thread geometry, and validated by existing pull-out experiments. For four screw failure modes, load-deformation behaviour and failure predictions of the two-scale method are verified by a detailed system model. Additionally, the two-scale method is validated for a combined load case by existing experiments, and demonstrated for different temperatures. Finally, the two-scale method is illustrated as part of a two-way coupled fire-structure simulation.

It was shown that proprietary ”threaded connection interaction” can predict thread relevant failure modes, i.e. thread failure, shank tension failure, and pull-out. For bearing, shear, tension, and pull-out failure, load-deformation behaviour and failure predictions of the two-scale method correspond with the detailed system model and Eurocode predictions. Related to combined load cases, for a variety of experiments a good correlation has been found between experimental and simulation results, however, pull-out simulations were shown to be inconsistent.

More research is needed before the two-scale method can be used under all conditions. This relates to the failure criteria for pull-out, combined load cases, and temperature loads.

The two-scale method bridges the existing very detailed small-scale screw models with present global-scale structural models, that in the best case only use springs. It shows to be insightful, for it contains a functional separation of scales, revealing their relationships, and it is computationally efficient as it allows for distributed computing. Furthermore, local small-scale non-convergence (e.g. a screw failing) can be handled without convergence problems in the global-scale structural model.

The extreme nature of fire makes structural fire engineering unique in that the load actions dictating design are intense and neither geographically nor seasonally bound. Simply, fire can break out anywhere, at any time and for any number of reasons. Despite the apparent need, the fire design of structures still relies on expensive fire tests, complex finite element simulations and outdated procedures with little room for innovation. This paper aims to discuss the aforementioned issues.

This primer highlights the latest state of the art in this area with regard to performance-based design in fire structural engineering. In addition, this short review also presents a series of examples of successful implementation of performance-based fire design of structures from around the world.

A comparison between global efforts clearly shows the advances put forth by European and Oceanian efforts as opposed to the rest of the world. In addition, it can be clearly seen that most performance-based fire designs are related to steel and composite structures.

In one study, this paper presents a concise and global view to performance-based fire design of structures from success stories from around the world.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

The purpose of this paper is to present a wall-climbing robot platform for heavy-load with negative pressure adsorption, which could be equipped with a six-degree of freedom (DOF) collaborative robot (Cobot) and detection device for inspecting the overwater part of concrete bridge towers/piers for large bridges.

By analyzing the shortcomings of existing wall-climbing robots in detecting concrete structures, a wall-climbing mobile manipulator (WCMM), which could be compatible with various detection devices, is proposed for detecting the concrete towers/piers of the Hong Kong-Zhuhai-Macao Bridge. The factors affecting the load capacity are obtained by analyzing the antislip and antioverturning conditions of the wall-climbing robot platform on the wall surface. Design strategies for each part of the structure of the wall-climbing robot are provided based on the influencing factors. By deriving the equivalent adsorption force equation, analyzed the influencing factors of equivalent adsorption force and provided schemes that could enhance the load capacity of the wall-climbing robot.

The adsorption test verifies the maximum negative pressure that the fan module could provide to the adsorption chamber. The load capacity test verifies it is feasible to achieve the expected bearing requirements of the wall-climbing robot. The motion tests prove that the developed climbing robot vehicle could move freely on the surface of the concrete structure after being equipped with a six-DOF Cobot.

The development of the heavy-load wall-climbing robot enables the Cobot to be installed and equipped on the wall-climbing robot, forming the WCMM, making them compatible with carrying various devices and expanding the application of the wall-climbing robot.

A heavy-load wall-climbing robot using negative pressure adsorption has been developed. The wall-climbing robot platform could carry a six-DOF Cobot, making it compatible with various detection devices for the inspection of concrete structures of large bridges. The WCMM could be expanded to detect the concretes with similar structures. The research and development process of the heavy-load wall-climbing robot could inspire the design of other negative-pressure wall-climbing robots.

Based on the weak seismic performance and low ductility of coupled shear walls, engineered cementitious composites (ECC) is utilized to strengthen it to solve the deformation problem in tall buildings more effectively and study its mechanical properties more deeply.

The properties of reinforced concrete coupled shear wall (RCCSW) and reinforced ECC coupled shear wall (RECSW) have been studied by numerical simulation, which is in good agreement with the experimental results. The reliability of the finite element model is verified. On this basis, a detailed parameter study is carried out, including the strength and reinforcement ratio of longitudinal rebar, the placement height of ECC in the wall limb and the position of ECC connecting beams. The study indexes include failure mode and the skeleton curve.

The results suggest that the bearing capacity of RECSW is significantly affected by the ratio of longitudinal rebar. When the ratio of longitudinal rebar increases from 0.47% to 3.35%, the bearing capacity of RECSW increases from 250 kN to 303 kN, an increase of 21%. The strength of longitudinal rebar has little influence on the bearing capacity of RECSW. When the strength of the longitudinal rebar increases, the bearing capacity of RECSW increases little. The failure mode of RECSW can be improved by lowering the casting height of the ECC beam in a certain range.

In this paper, ECC is used to strengthen the coupled shear wall, and the accuracy of the finite element model is verified from the failure mode and skeleton curve. On this basis, the casting height of the ECC casting wall limb, the strength and reinforcement ratio of longitudinal rebar and the position of the ECC beam are studied in detail.

This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

The purpose of this study is to identify the factors leading to adoption of social networking sites (SNS) by librarians of entrepreneurial libraries to provide support to business community in their market research. Identification of these adoption factors will help to design SNS in the way that led to their seamless diffusion in university libraries, making them capable of providing reliable and useful market knowledge to the business community.

To meet the research objectives, this study is based on the technology adoption model (TAM). The survey was conducted with 50 librarians of various universities across Europe, Asia, America, Africa and Australia, sampled through non probabilistic sampling techniques-purposive and snowball sampling. The collected data are analyzed through partial least squares structural equation modeling (PLS-SEM) to estimate the formulated structural equation model using SmartPLS 3.2.9 software version.

The results indicate that university librarians have high perception about usefulness and ease of using SNS for meeting their business objectives. The perceived usefulness (PU) is solely based on the prolonged adoption of SNS technology in personal lives of the librarians. The perceived ease of use (PEU) is triggered by the increasing quality of evolving SNS technology, especially user friendliness. The prolonged use of technology, increasing market entry of brands and improving quality of SNS will overcome librarian resistance to use SNS for market research related support to business community. Evolving technologies are more likely to be adopted seamlessly in entrepreneurial libraries.

This study has implications for librarians, entrepreneurs and technology providers. Librarians should have flexible technology adoption policies in place to keep technology adoption synchronized with changing market conditions. Entrepreneurs should share clear and accurate business needs as well as practicing ethical and lawful business practices. They should also help libraries to gain new skills to make future adoption easier. Technology providers should focus on improving the quality of their technology by increasing their functional utilities, performance and reliability as well as making them more user-friendly.

This study investigates technology adoption of SNS in libraries from the perspective of providing market research related services to entrepreneurs rather than just digitalizing library services for student users.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

This study aims to examine the relationship between training and development (TD) of human talent and innovative performance (IP) while considering the mediating role of knowledge generation (KG).

An explanatory design with latent variables was used, represented through a system of structural equations made up of observable and latent variables. The study had a sample of 1,572 companies belonging to the Colombian manufacturing industrial sector between 2017 and 2018. The instrument used in this research was the survey on technological development and innovation in the manufacturing industry EDIT IX. The statistical approach for estimating the parameters of the structural equation model (SEM) was the variance-based or PLS-SEM approach.

The results link the existing theory on the relationship between TD of human talent and IP with the empirical evidence in Colombian manufacturing companies. In addition, the research highlights the role of KG in the relationship presented, which leads to the conclusion that it is an essential factor for any organization today.

The findings obtained are practical use for the various agents involved in the management of manufacturing companies. At present, it is not only important for companies to generate good economic returns. It is necessary to focus on IP, which will favor the economic and non-economic firm performance, which involves the improvement in the quality of the products and services offered.

This paper aims to identify and report the differential effects of activity control and capability control on role stressors, which subsequently affect salespeople’s job satisfaction and sales performance.

Drawing on job demands-resources (JD-R) theory, the authors defined active control and customer demandingness as the job demands and capability control as the job resource, and designed their relationship with role stressors, which are indicated as role ambiguity, role conflict and role overload. The authors enrolled a sample of 223 industrial salespeople from pharmaceutical companies. After collecting the data, the authors used structural equation modeling using AMOS to test and estimate causal relationships along with a two-step approach to examine the interaction effect. The authors have also tested the simple slope of two-way interactions. All of the measured variables were identical to those used in previous studies.

The study findings indicate that behavior-based control can be counterproductive. Reducing activity control can decrease role stress, increase job satisfaction and improve job performance; increasing capability control, however, can reduce role stress and increase job satisfaction and performance. It is also important to acknowledge the external environment of the sales context in which behavior-based control is most effective: whereas high customer demandingness and capability control are related to reduced role stress, high customer demandingness and activity control are related to increased role stress.

Sales managers should recognize that different control management regimes reinforce or mitigate salespeople’s job stressors and outcomes under specific conditions (i.e. work environments marked by higher or lower customer demandingness).

Drawing on JD-R theory, the research shows that a behavior control (i.e. activity control and capability control) has differential, and even opposite, psychological consequences.