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The purpose of this paper is to summarize the research of construction conflict from 1991 to 2020 and propose research directions for future scholarly work. During the recent decades, it is widely accepted that construction conflict is inevitable, and conflict management has become an important component of project management. However, few works were done to map the global study in this field, there is limited review that evaluates the current stage of construction conflict research.

This study adopted a holistic literature review approach that incorporates bibliometric search and scientometric analysis. A total of 698 bibliographic records from the Web of Science core collection database were collected for the scientometric analysis. CiteSpace5.7 was adopted for the science mapping purpose in this study.

Through co-authorship analysis, co-word analysis and co-citation analysis, influential scholars and journals are identified. Several research trends are highlighted according to the scientometric analyses of the construction conflict topics. For example, the application of simulation and algorithms to the study of construction conflict management systems.

Construction is a resource-intensive, multi-participant and multi-targeted industry. Conflicts always exist in the whole life cycle of construction projects, it is important for industry practitioners to be updated of the latest movement and progress of the academic research.

This study contributed to the body of knowledge in construction conflict and bridge the research gap in the thorough review of previous research work.

The expected benefits of newly developed transportation infrastructures are the saving of travel time and further promoted transport economics. There is a need for a methodology of travel time estimation with acceptable robustness and practicability. Macroscopic fundamental diagram (MFD) represents the overall traffic performance at a network level by linking average flow, speed and density. MFD can be used to estimate network state and to describe various traffic management strategies. This study aims to describe the effect of new infrastructure development on the network performance using the MFD framework.

The scenarios of Islamabad Road network before and after the infrastructure construction were simulated, in which the floating car data set (FCD) for multiple modes was extracted. MFD has been formed for the whole region and partitioned region, which was divided on the basis of infrastructural changes. Moreover, this study has been extended to calculate travel time for multiple modes using the MFD results and the Bureau of Public Roads (BPR) function at a neighborhood level.

MFD results for the whole network showed that the speed of traffic improves after the construction of new infrastructure. The travel time estimates using MFD results were dependent on the speed estimates, whereas the estimates obtained using the BPR function were found to be dependent on the traffic volume variation during different intervals of the day. By using the FCD for multiple modes, travel time estimates for multiple modes were obtained. The BPR function method was found valid for estimating travel time of traffic stream only.

This paper innovatively investigates the change in network performance for pre-construction and post-construction scenarios using the MFD framework. In practice, the approach presented can be used by transportation agencies to evaluate the effect of different traffic management strategies and infrastructural changes.

At the US passenger stations, train operations approaching terminating tracks rely on the engineer’s compliant behavior to safely stop before the end of the tracks. Noncompliance actions from the disengaged or inattentive engineers would result in hazards to train passengers, train crewmembers and bystanders at passenger stations. Over the past decade, a series of end-of-track collisions occurred at passenger stations with substantial property damage and casualties. This study’s developed systemic model and discussions present policymakers, railway practitioners and academic researchers with a flexible approach for qualitatively assessing railroad safety.

To achieve a system-based, micro-level analysis of end-of-track accidents and eventually promote the safety level of passenger stations, the systems-theoretic accident modeling and processes (STAMP), as a practical systematic accident model widely used in the complex systems, is developed in view of environmental factors, human errors, organizational factors and mechanical failures in this complex socio-technical system.

The developed STAMP accident model and analytical results qualitatively provide an explicit understanding of the system hazards, constraints and hierarchical control structure of train operations on terminating tracks in the US passenger stations. Furthermore, the safety recommendations and practical options related to obstructive sleep apnea screening, positive train control-based collision avoidance mechanisms, robust system safety program plans and bumping posts are proposed and evaluated using the STAMP approach.

The findings from STAMP-based analysis can serve as valid references for policymakers, government accident investigators, railway practitioners and academic researchers. Ultimately, they can contribute to establishing effective emergent measures for train operations at passenger stations and promote the level of safety necessary to protect the public. The STAMP approach could be adapted to analyze various other rail safety systems that aim to ultimately improve the safety level of railroad systems.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

Existing studies mostly rely on the static characteristics of team members, and there is still a lack of empirical investigation on how entrepreneurial team members make decisions through dynamic team process and how team members’ cognition influences team decision-making. The purpose of this study is to validate how entrepreneurial team heterogeneity affects team decision-making performance from the perspective of dynamic team process.

Drawing on the theory of input-process-output model, this study proposed and examined the mediating role of team interaction as well as the moderating role of proactive socialization tactics in the relationship between entrepreneurial team heterogeneity and decision-making performance. Based on a sample of 162 entrepreneurial teams that include pairing superiors and subordinates, hierarchical regressions and moderated mediation tests were used to test the hypotheses.

The research results show that the heterogeneity of entrepreneurial teams is positively correlated with both team interaction and decision-making performance. Team interaction plays a mediating role between entrepreneurial team heterogeneity and decision-making performance; information seeking of proactive socialization tactics moderates the impact of entrepreneurial team heterogeneity on team interaction.

Contributing to the literature on entrepreneurial team decision-making performance, this study identifies that proactive socialization tactics with a high level of information seeking can help entrepreneurial team members respond to environmental and organizational changes more effectively during team development and increase the effectiveness of team interaction. This finding helps us better understand the mechanism and context under which entrepreneurial heterogeneity may enhance the team’s decision-making performance.

International construction joint ventures (ICJVs) have been widely used as a temporary arrangement in many projects all over the world, especially in megaprojects. Within ICJVs, the national culture difference between partners affects their cooperation significantly. However, prior research has provided contradictory empirical evidence regarding these impacts. To address this problem, the purpose of this paper is to introduce cultural intelligence that judges an individual’s capability to function and manage effectively in culturally diverse settings as a moderating variable.

Multiple regression analysis and moderated multiple regression were undertaken to test proposed hypotheses. A questionnaire survey was conducted with international construction practitioners who had experiences of managing or participating in ICJVs.

The result of multiple regression analysis revealed that difference in national culture has significant negative effects on information exchange, shared problem solving and flexibility when asking for changes, thus effecting cooperation within ICJVs. Meanwhile, cultural intelligence of members can weaken these negative influences.

First, given that national culture difference affects negatively on the cooperation within ICJVs, it needs to be regarded as one of vital resources of risk which should be prevented and managed when attending ICJVs; Second, managers should build a series of mutually agreeable regulations and rules to lessen the negative effect of national culture difference; Third, it is recommended that ICJV management teams contain as many work-experienced members as possible and members within ICJVs, especially new staff, receive cross-cultural training termly to facilitate the cooperation between partners.

This research reveals the moderating effects of cultural intelligence on the relationship between national culture difference and cooperation in ICJVs as well as provides practical implications for ICJV managers to deal with national culture difference and reduce its negative impact on cooperation within ICJVs.

Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.

In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.

The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types

This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

The purpose of this study is to address the extended travel time caused by dwelling time at stations for passengers on traditional rail transit lines. To mitigate this issue, the authors propose the “Non-stop” design, which involves trains comprised of modular vehicles that can couple and uncouple from each other during operation, thereby eliminating dwelling time at stations..

The main contributions of this paper are threefold: first, to introduce the concept of non-stop rail transit lines, which, to the best of the authors’ knowledge, has not been researched in the literature; second, to develop a framework for the operation schedule of such a line; and third, the author evaluate the potential of its implementation in terms of total passenger travel time.

The total travel time was reduced by 6% to 32.91%. The results show that the savings were more significant for long commutes and low train occupancy rates.

The non-stop system can improve existing lines without the need for the construction of additional facilities, but it requires technological advances for rolling stock.

To eliminate dwelling time at stations, the authors present the “Non-stop” design, which is based on trains composed of locomotives that couple and uncouple from each other during operation, which to the best of the authors’ knowledge has not been researched in the literature.

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

To maintain the bottom line of food import risk in China, this paper proposes a novel risk state evaluation model based on bottom-line thinking after analyzing the decision-making ideas embedded in the bottom-line thinking method.

First, the order relation analysis method (G1 method) and Laplacian score (LS) are applied to calculate the constant weights of indexes. Then, the worst-case scenario of food import risk can be estimated to strive for the best result, so the penalty state variable weight function is introduced to obtain variable weights of indexes. Finally, the study measures the risk state of China's food import from the overall situation using the set pair analysis (SPA) method and identifies the key factors affecting food import risk.

The risk states of food supply in eight countries are in the state of average potential and partial back potential as a whole. The results indicate that China's food import risks are at medium and upper-medium risk levels in most years, fluctuating slightly from 2010 to 2020. In addition, some factors are diagnosed as the primary control objects for holding the bottom line of food import risk in China, including food output level, food export capacity, bilateral relationship and political risk.

This paper proposes a novel risk state evaluation model following bottom-line thinking for food import risk in China. Besides, SPA is first applied to the risk evaluation of food import, expanding the application field of the SPA method.