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In this paper, we discuss the need for accurate skills assessments of employees in large, global, client-facing enterprises and shortcomings of existing systems for obtaining and managing expertise. We describe enterprise and social data that can be mined to improve skill assessment processes. We propose a matrix completion approach with side information for improved skill assessment prediction and recommendation, and discuss how outputs can improve existing business process use cases and illuminate new ones.

Introduction. Most methods used to develop construction risk responses address the risk-mitigation optimization problem by solving the objective functions. They are passively achieved by satisfying...

The COVID-19 pandemic has caused significant economic and social impacts worldwide and revealed the importance and urgency of urban resilience enhancement. To learn the lessons from this pandemic, this study undertook a case study in Wuhan City, which was the first city in China where the outbreak was declared, and analyzed the process of its response to the pandemic. Based on the theory of emergency management, this study identified four stages of the emergency management process and conducted an in-depth analysis of the experience gained at each stage from the perspective of urban resilience. Then, this study evaluated the level of urban resilience of Wuhan City, and put forward suggestions for enhancement of the emergency system of China. The findings provided important revelation for other cities to cope with major disasters in the future.

Resistance to unexpected disasters and rapid post-disaster recovery (i.e., disaster resilience) of cities are extremely necessary owing to the concentrated risk of urbanization. Resilience quantification can adequately reflect the capacity of a city to withstand disasters. Many existing studies have focused on and proposed several frameworks on the quantitative measures of disaster resilience, and the corresponding research objects include different types of disasters (e.g., earthquake, hurricane, flood, and fire), various domains (e.g., engineering, social, and economic), and multiple levels (e.g., city, community, and building). Among these research objects, studies on seismic resilience in civil engineering are relatively comprehensive. Specifically, studies on resilience in civil engineering have paid significant attention to the dynamics of engineering facilities and the engineering-related social and economic functions, including city-scale engineering, social, and economic functionalities, and essential functionalities of building, transportation, lifeline, and nonphysical subsystems of a city. Consequently, based on the review of resilience studies carried out in recent years, the framework and specifications for the quantification of disaster resilience of civil engineering systems subjected to earthquakes and other unexpected disasters are elaborated. Methods of disaster resilience assessment of cities and the corresponding subsystems are discussed. Furthermore, several case studies are reviewed, and resilience limit-state analyses of communities and buildings are performed.

Business interruption ratio (BIR), the ratio of reduced production or service level of a business to the predisaster level when it reopens in the aftermath of a disaster event, is a signifi...

While previous studies have extensively explored the neural mechanisms of perceptual decision-making, most of them used paradigms with limited real-life consequences and largely neglected participants' individual differences. In this study, to resemble a perceptual decision-making scenario with real-life consequences, construction workers were recruited for an occupational hazard recognition task by categorizing construction site images as hazardous or safe with their EEG recorded. Event-related potential (ERP) analysis revealed distinct influences of perceptual decision-making by two dispositional factors of risk propensity and injury exposure experience. Risk propensity was positively correlated with the stimulus-locked difference waveforms (hazardous minus safe) at approximately 200 ms post-stimuli-onset over right-lateralized parietal-occipital areas. The difference waveforms showed reversed polarity between groups with high and low-risk propensity. Injury exposure experience was negatively correlated with the response-locked difference waveforms approximately 450 ms before motor response over right-lateralized parietal-occipital regions. To the best of our knowledge, this study is the first to report how individuals' injury exposure experience influenced the neural signatures of one's perceptual decision-making. These results extended previous findings for perceptual decision-making by setting up a scenario with high ecological validity and suggested possibly substantial different mechanisms for individual workers by the intrinsic factor of risk propensity and the extrinsic factor of injury exposure experience.

With the increase of mechanization in the construction industry, more and more construction machines appear on construction sites. This improves construction efficiency but leads to the high risk of human-machine collision. At present, human-machine collision risks are usually monitored and warned manually. It is difficult to identify relevant risks in time due to the lack of sufficient safety supervisors. This paper proposes an efficient human-machine collision warning system based on computer vision and deep learning. The designed system and relevant devices are installed on construction machinery. It can quickly identify workers and measure the distance between workers and machinery in the complex and dynamic environment so as to quickly warn the risk of human-machine collision based on a threshold. Power supply restriction and harsh environment such as dust and vibration are also considered to ensure the stable and effective operation of the system. The system was tested on a construction site for 6 weeks. The result shows that the system has a significant positive effect on the early warning of human-machine collision risk, thus benefiting the improvement of safety performance of workers in human-machine collaboration scenes.

Theoretically, prevention through design (PtD) is the most effective approach for eliminating human error in the field of construction. However, there is a lack of rigorous empirical analysis regarding the focal point of PtD. Hence, this research aims to develop a system dynamics model that depicts a mechanism for identifying the focal point of PtD and relevant design factors with respect to human error. Elevator installation serves as the scope of the model, and three sub-models of “cognition-behavior,” “environment-cognition,” and “design-environment” are developed. Their quantitative relationships are analyzed based on empirical data prior to the development of the system dynamics model. Subsequently, safety performance is simulated based on an eight-hour work day, and sensitivity analyses are conducted to determine the impact of each design factor on human error. This study provides insights into PtD using various design factors that can be generalized and used to develop safer designs under complex working environments.

Pervasive unsafe behaviors of construction workers are the primary cause of accidents on construction job sites. The workers' safety-related behaviors are subject to a variety of factors, such as interactions with coworkers and interventions by management teams. The impacts of these factors have attracted considerable attention in academia but are yet to be fully examined. To provide a quantitative assessment of these impacts and their managerial implications in practice, an agent-based modeling approach of construction safety-related behaviors, which adopts a bottom-up architecture, is proposed in this research. This approach regards safety performance as an emergent property of the behaviors and interactions of construction workers and management teams. The development of the agent-based model is based on a range of theoretical and empirical evidence, including a cognitive analysis model for modeling worker behaviors, and site observations and surveys for the design of simulated working environment and tasks, as well as the modeling of individual behaviors and interactions. Four managerial scenarios are simulated using the proposed approach. Based on the simulation results, the effects of a number of managerial factors on the safety performance of construction workers are examined. These factors include duties of supervisors, the competency and inspection strategy of safety officers, the frequency of safety training, senior managers' involvement in safety activities and emphasis on safety goals. The effects of these factors are examined in a quantitative manner, and relevant implications for construction safety management practice are discussed. The findings prove that agent-based modeling is an effective approach for analyzing the characteristics and patterns of construction safety-related behaviors and assessing possible safety management strategies.

Occupational health and safety (OHS) is of the utmost concern in the construction sector. For decades, researchers and practitioners have endeavoured to enhance construction OHS performance through various measures ranging from “hard” technologies (in this paper, the “first wave” of construction OHS management) such as provision of personal protective equipment, to the more recent “soft”, managerial approaches (the “second wave”) such as fostering a safety culture. Although considerable improvements have been made in construction OHS, the general sentiment is that construction remains one of the most dangerous sectors, warranting more innovative or even revolutionary approaches. This research seeks to develop a smart construction object (SCO)-enabled OHS management system. The central tenet of the system is that artificial intelligence (AI), as the art of creating machines that perform functions that require intelligence when performed by people, represents a direction of the “third wave” in construction OHS management. The system embraces emergent SCOs and harnesses the power of their smart properties of awareness, communicativeness, and autonomy. The system is demonstrated and validated in real-life construction practice and a controlled lab test with a tower crane, the cause of many construction-related injuries and fatalities, as the subject. It is found that the SCO-enabled OHS management system can identify dangerous situations and respond to them autonomously. This research suggests that smarter construction, through incorporation of AI in particular, is a direction of much promise in terms of improving construction OHS.

Corporate social responsibility (CSR) reporting is gaining importance in the construction industry as an effective tool to communicate social citizenship. Despite the increasing attention o...

Achieving the Sustainable Development Goals (SDGs) is a long-term task, which puts forward high requirements on the sustainability of related policies and actions. Using the text analysis method, we analyze the China National Sustainable Communities (CNSCs) policy implemented over 30 years and its effects on achieving SDGs. We find that the national government needs to understand the scope of sustainable development more comprehensively, the sustained actions can produce positive effects under the right goals. The SDGs selection of local governments is affected by local development levels and resource conditions, regions with better economic foundations tend to focus on SDGs on human well-being, regions with weaker foundations show priority to basic SDGs on the economic development, infrastructures and industrialization.

Hospitals play a crucial role in providing badly needed medical care after earthquakes. Meanwhile, hospitals are likely to find themselves subject to earthquake impacts and may fail to func...

The city is gradually becoming the most significant carrier of current economic society, in which critical infrastructures (CI), such as transportation, energy, and healthcare, are backbones for the operation of cities. In the meantime, it’s not uncommon in our cities to see traffic congestion, shortage of resources, or environmental pollution, with the ever-growing population of cities and. Besides, global warming and climate change also have a crucial impact on urban life, through events such as frequent urban floods. With the advent of the fourth industrial revolution, technological advancements such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), cloud computing, cognitive computing, etc., are being implemented to make cities more instrumented, interconnected and intelligent. This chapter reviews smart city requirement, components and applications and discusses two of its subdivisions, namely smart transportation on smart cities and smart construction in the smart cities.

Leadership is proven as a key factor impacting safety while researchers and practitioners are fostering proactive approaches to preventing workplace injuries. Practitioners' lack of leadership is one of the major causes for the continuous high-level accident frequency within the construction projects. An important yet still unsettled academic issue is how leadership impacts safety performance of construction projects, and how safety leadership can be improved. In order to probe into the mechanism by which leadership improves project safety, this study develops a safety leadership model for construction projects (SLMCP) in both theoretical and pragmatic perspectives. Theoretically, this model incorporates specific characteristics of construction projects and applies a multiple levels-of-management perspective to depict leadership's cascading influences across project stakeholders. Safety culture and safety management are the two major paths by which leadership impacts safety performance. Pragmatically, the action research (AR) method is used to validate the theoretical model empirically and develop feasible measures to implement safety leadership in practice. A five-round longitudinal evaluation confirmed in a case study the validity of SLMCP and the effectiveness of safety leadership improvement measures. This paper contributes to the body of knowledge by clearly depicting safety leadership's cascading top-down influencing mechanism in construction projects and providing concrete and validated measures for leadership improvement. The AR based intervention also establishes a general procedure for leadership promotion in practice. Conclusions of the paper serve as novel ideas and methods for workplace safety improvement in construction projects.