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The purpose of this paper is to provide a systematic method to perform analysis and test for vibration‐thermal strain behavior of plastic ball grid array (PBGA) assembly by considering thermal and vibration loading mode. Also to investigate the dynamic behavior of PBGA assembly by considering loading modes for design and reliability evaluation of PBGA packaging.

A PBGA assembly prototype with different structure and material parameters is designed and manufactured. Based on investigation of the structural and physical parameters of PBGA sample, the vibration‐thermal strain test is developed to measure the strain distribution at the surface of the BT (bismaleimide triazine) substrates and PCB (printed circuit board) surface under vibration‐thermal cycling loading such as random vibration and the temperature is changed from 0°C to 100°C.

The test results show that the loading modes have different impact on PCB, EMC and substrate, respectively. In the meantime, it is shown that the characteristics of the compound mode is not the linear accumulative result by single vibration mode and single thermal loading mode as forecasted. The nonlinear mechanism for these modes application is the future work for progress.

It is very difficult to set up a numerical approach to illustrate the validity of the testing approach because the complex loading modes and the complex structure of PBGA assembly. The research on an accurate mathematical model of the PBGA assembly prototype is a future work.

It implies a potential design characteristic for future application of PBGA assembly. It also builds a basis for future work for design and reliability evaluation of BGA package.

This paper fulfils useful information about the thermal‐vibration coupling dynamic behavior of PBGA assembly with different structure characteristics, materials parameters.

This study aims to explore how project-based firms, which generally organize most of their work around temporary projects in discontinuous and fragmented types of business contexts, proactively formulate and implement digital transformation strategies under institutional pressures in a predigital era.

An exploratory case study was conducted in a large-scale construction company in China using multiple data collection methods, including semistructured interviews, documentation collection and observation.

An integrated framework is developed to conceptualize three key dimensions of digital transformation strategies of project-based firms: strategic adaptation for organization-environment fit through balancing the internal efficiency needs with the external legitimacy pressures; proactive business transformation through comprehensively managing the roles of digital technologies in optimizing defined business processes and fostering new business models; and delicate organizational transformation to integrate temporary project-level operation processes with ongoing firm-level business processes.

This study represents an exploratory effort to empirically investigate how project-based firms strategically organize complex digital transformation imperatives in their discontinuous and fragmented business contexts. The findings contribute to deepened understandings of how complex organizational and environmental contexts can be comprehensively managed for systemic business and organizational transformations to leverage the value of emerging digital technologies for project-based organizations.

Few of the established risk identification methods refer to low-severity yet high-frequency safety risks data that may lead to several safety risks being ignored, thus reducing the potential of learning from a considerable number of cases. The purpose of this study is to explore a new valid method based on preaccident safety supervision data to identify these minor construction safety risks during routine construction operations.

A total of 329 official construction safety supervision reports containing 5,159 safety problem records from Shanghai between 2016 and 2018 served as raw material for in-depth analysis. Given the characteristics of the data collected, text mining integrated with natural language processing was applied to review the supervision reports and group safety risks automatically.

This study clarifies the way in which the supervision data should be employed to analyze high-frequency–low-severity safety risks. From these data, seven unsafe-act-related and nine unsafe-condition-related risks are identified. Regarding unsafe-act-related risks, inappropriate human behaviors could usually occur in personnel management, contract management, expense management, material management and acceptance work. For unsafe-condition-related risks, hoisting, scaffolding and reinforcement works are the main generators of onsite safety hazards during construction operations.

The study includes implications for project managers and supervisors to facilitate more effective proactive risk management by paying more attention to collecting and employing the supervision data established in each routine inspection.

Whereas previous research focused on analyzing severe accidents, this study seeks to identify the high-frequency–low-severity construction safety risks using the preaccident supervision data. The findings could provide a new thought and research direction for construction safety risk management.

Non‐planarity of assemblies and co‐planarity variation effects on anisotropic conductive adhesive (ACA) assemblies have been a concern for ACA users since the materials are first devised. The primary objective of this paper is to introduce a new experimental method for studying co‐planarity variation effects on ACA assemblies.

The approach simulates non‐planarity through deliberate chip rotation during the ACA bonding process, thereby locking different levels of co‐planarity variation into ACA test assemblies. Scanning electron microscope (SEM) analysis and electrical joint resistance measurement using the four wire resistance (FWR) method are used to mechanically and electrically examine the connection quality of the ACA assemblies bonded with non‐planar joints, for which the chip and substrate patterns are specially designed to allow joint resistance measurement using the FWR method.

Typical experiments and their results are presented and analysed. The bond thickness differences between the SEM measurements and calculations indicate that the real rotations are smaller than those predicted by the calculations. The typical experimental results show that the joint resistance reduces as the deformation increases until reaching a relatively stable value after a certain deformation degree.

The average joint resistances in the rotated samples are all bigger than those measured in the un‐rotated samples. This raises the question as to whether the joint resistances of ACA assemblies are more significantly affected by other affects of non‐planarity than just by its effect on bond thickness. However, before this can be confirmed, more research must be done to check if this behaviour happens for different bonding forces.

This paper reports a novel and simple experiment that can be used to examine the effects of co‐planarity variation on the electrical performance of ACA assemblies, by creating different bond thicknesses that are normally difficult to achieve by changing the bonding pressure, since ACA bond thicknesses are not linearly related to the bonding force. The merit of the technique is that there is no need to manufacture chip bumps and substrate pads with different geometries, or to control the bond pressure, to achieve bond thickness variation in ACA assemblies.

As the foundation of social and economic development, infrastructure development projects are characterized by large initial investment, high technical requirements and thus generally delivered through complex contractor–subcontractor collaboration chains. This study aims to characterize the complexity of collaborative networks between contractors and subcontractors for infrastructure development through comparing the structural characteristics and the formation mechanisms of contractor–subcontractor collaborative networks for the following two different types of infrastructure: public works (PWCN) owned and operated by government agencies, and public utilities (PUCN) owned and operated by nongovernment agencies.

Based on the method of stochastic actor-oriented models and the longitudinal dataset of National Quality Award Projects in China during 2001–2020, this study compares how the structural characteristics of project-based collaborative networks between contractors and subcontractors for the two types of projects are different and how related micro-mechanisms, including both structure-based endogenous network effects and attribute-based exogenous homophily effects (institutional, organizational and geographical homophily), collectively underpin the formation of the networks.

The empirical results provide evidence that while the two networks are both characterized by relatively low levels of network density, PWCN is more globally connected around a minority of superconnected contractors as compared with PUCN. The results further reveal that compared with PUCN, the formation of PWCN is more significantly related to the structure-based anti in-isolates effect, suggesting that PWCN is more open for new entrant subcontractors. With regard to the attribute-based homophily effects, the results provide evidence that while both significantly and positively related to the effects of organizational (same company group) and geographical homophily (same location), the formation of PWCN and PUCN is oppositely driven by the institutional homophily effect (same ownership type).

As an exploratory effort of using network perspective to investigate the formation mechanisms of contractor–subcontractor relationships in the infrastructure development domain, this study contributes to a network and self-organizing system view of how contractors select subcontractors in different types of infrastructure projects. The study also provides insights into how contractor–subcontractor collaborative relationships can be better manipulated to promote the development of complex infrastructure in different contexts.

Due to the lack of industrywide data, now there is only perceptual knowledge, through survey data, that construction firms may face great challenges in geographical diversification. The article aims to provide an objective description on the state of interregional market entry practices in the Chinese construction industry. How these practices are influenced by related set of regional and industry-related factors is further studied.

Description analysis of the practices is first performed based on a first-hand industrial data set including 1,020 projects and 404 construction firms. Combining provincial panel data in the National Bureau of Statistics of China, related regional and industry-related factors on these practices are further analyzed through hierarchical regression models.

(1) Interregional market entry practices are not quite prevalent during the past two decades, only involving 271 projects and 111 firms which accounts for 26.57% and 27.47% of the analyzed subjects respectively; (2) Large, state-owned and experienced firms are more frequently involved in the practices; (3) A number of regional factors such as local market protection and industry scale also significantly impact the practices.

The empirical analysis of this study was conducted using the specific dataset from the National Quality Award Projects where firms tend to be national, which may limit the generalizability of the findings to the whole industry contexts.

While most of the research on market entry practices in the project-based construction industry is undertaken at project or firm level based on survey data, this research represents an exploratory effort of using objective data to provide a macro overview of the practices at the industry level. The findings could contribute to a deepened understanding of how these practices are impacted by related regional- and industry-level factors.

The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.

A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.

The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.

MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

This study aims to identify the enabling factors for Building Information Modeling (BIM) adoption in Vietnamese construction enterprises and uncover their interrelationships. This will help stakeholders focus on controlling and allocating resources (time, personnel, and costs) appropriately to adopt BIM and differentiate themselves from fierce competition in the architectural, engineering, construction and operations (AECO) industry.

This study first identifies and evaluates 32 enabling factors for applying BIM in the Vietnamese construction industry according to the TOE extended framework. Afterwards, a hybrid questionnaire survey using a convenient sampling method is conducted to capture stakeholders' views. The exploratory factor analysis (EFA) and the partial least squares structural equation modelling (PLS-SEM) technique are then applied to identify the constructs of the enabling factors and their interrelationships.

The study extracts six constructs that could have a significant impact on the adoption of BIM in construction enterprises, namely: technical feasibility (TF), human resources and management (HRM), company business vision (CBV), political environment (PE), economic viability (EV), and legal aspects (LA). Based on eleven proposed hypotheses, the analysis results confirm nine hypotheses and show that the HRM, TF, and CBV have the strongest effects on managers in evaluating the factors for BIM.

The results of the study fill the gap in knowledge by discovering the interrelationships among the enabling factors for BIM adoption in construction enterprises. The results might support the construction enterprises and their stakeholders in increasing the application of BIM, and digital transformation in construction industry.