Disaster Relief Organizations Research Articles

Crowdsourced geolocation: Detailed exploration of mathematical and computational modeling approaches

In emergency situations, social media platforms produce a vast amount of real-time data that holds immense value, particularly in the first 72 h following a disaster event. Despite previous efforts, efficiently determining the geographical location of images related to a new disaster remains an unresolved operational challenge. Currently, the state-of-the-art approach for dealing with these first response mapping is first filtering and then submitting the images to be geolocated to a volunteer crowd, assigning the images randomly to the volunteers. In this work, we extend our previous paper (Ballester et al., 2023) to explore the potential of artificial intelligence (AI) in aiding emergency responders and disaster relief organizations in geolocating social media images from a zone recently hit by a disaster. Our contributions include building two different models in which we try to (i) be able to learn volunteers’ error profiles and (ii) intelligently assign tasks to those volunteers who exhibit higher proficiency. Moreover, we present methods that outperform random allocation of tasks, analyze the effect on the models’ performance when varying numerous parameters, and show that for a given set of tasks and volunteers, we are able to process them with a significantly lower annotation budget, that is, we are able to make fewer volunteer solicitations without losing any quality on the final consensus.

The influence of trauma on internal integration: An approach‐avoidance analysis in disaster relief operations

Disaster relief workers face unique factors in their operating environments that can inhibit internal integration. For example, disaster relief often involves exposure to traumatic events affecting relief workers’ commitment to cooperation and the organization. As such, disaster relief organizations dedicate substantial amounts of scarce resources to support workers exposed to trauma. Unfortunately, contradictory views exist in the literature on how trauma exposure affects commitment and integrative behaviors and how supervisor support influences these relationships. Based on the approach‐avoidance coping theory, we test whether trauma exposure has positive or negative effects. We test our hypotheses on data from 300 disaster relief workers collected using a 2 × 3 factorial scenario‐based experiment. We find that trauma exposure evokes avoidance coping behaviors, which decrease individuals’ cooperative disposition, and approach coping behaviors, which motivate organizational commitment. Next, we show that both forms of commitment have a nonlinear convex relationship with internal integration and mediate the relationship between trauma exposure and internal integration. Finally, we find that supervisor support amplifies these relationships. When exposed to trauma, supervisor approach and avoidance orientations provide higher internal integration levels than subjects exposed to no supervisor support. These findings extend the literature on disaster relief management, integration, and support, guiding decision‐making regarding support investments in disaster relief organizations.

Speak with One Voice? Examining Content Coordination and Social Media Engagement During Disasters

Speak with One Voice? Examining Content Coordination and Social Media Engagement During Disasters Practice- and policy-oriented abstract: Disaster relief organizations (DROs) use social media to share information rapidly and broadly. Many DROs maintain multiple accounts on the same social media platform. Each account represents a different operational entity of a DRO, such as its national headquarters or a local branch. An important problem that DROs with multiple accounts face is how to coordinate the production of social media content across these accounts. One strategy is to have all accounts within the same DRO match their decisions about how content is created and designed (e.g., audience, topic). An alternate strategy is to mismatch these decisions. Using Twitter data collected in partnership with the Canadian Red Cross, we analyze which coordination strategy is best for social media engagement. Our results suggest that, during the immediate aftermath of a disaster, accounts within the same DRO should produce content with similar characteristics by matching their content creation decisions. This leads to a 4.3% lift in engagement. However, when DROs start working on helping impacted communities recover from disasters, engagement is 29.6% higher when their accounts mismatch their content creation decisions and post distinctive content.

Inter-Organizational Partnering Strategies in Disaster Response: A Complex Network Perspective

Establishing appropriate inter-organizational partnership in disaster response is of great help to the improvement of disaster relief performance. However, the selection of proper disaster response organizations for government agencies to build partnerships among the numerous relief organizations remains to be addressed. To fill the gap, this paper adopted a complex network modeling method to explain the formation mechanism of inter-organizational partnership in disaster response. Then, based on a numerical simulation method, the paper compared different inter-organizational partnering strategies, and explored the optimal inter-organizational partnering strategy in the disaster response process. Results demonstrate that the optimal partnering strategy is contingent on disaster response conditions, with accurate disaster information and abundant relief materials to choose the material difference principle, and vague disaster information to choose the organizational influence principle. At the same time, frequent information communication and material cooperation allocation can be transformed into low-frequency information communication and government-led material allocation. This study provides practical guidance for disaster relief organizations to choose inter-organizational partnering strategies, and enriches the disaster organization management theory.

Spontaneous volunteers and the flood disaster 2021 in Germany: Development of social innovations in flood risk management

AbstractIn the response of the flood disaster in Germany 2021 a vast number of spontaneous volunteers (SVs) participated. They organized coordinating services, developed emergent forms of SVs and thus hold the potential to improve existing flood managing capacities. This raises the need for in‐depth knowledge about SVs and organization, the use of information and communication technologies (ICT), social and individual aspects. This article analyzed results of an online survey (n = 2636) with the aim to discuss challenges and potentials to develop SVs as social innovations in flood risk management. As a result, SVs are strong value‐based motivated, can be psychologically stressed and travel long distances, especially when they got aware of helping possibilities via social media. Disaster relief organizations rarely cooperate with or integrate SVs, but when they collaborate, SVs' satisfaction with authorities increases significantly. Based on the results, this article derives preplanning of useful concepts consider safety aspects, ICT utilization, foster mutual respect between SVs and authorities and supporting sense of community as key results for development of social innovations. Especially in light of pandemics, social crisis like refugee movements, and climate change‐related disasters, the requirement rises for engaging SVs.

Social media data extraction for disaster management aid using deep learning techniques

The process of minimizing the damage being caused by disasters through information gathering, information sharing, disaster planning, problem-solving, and decision-making is known as disaster management. Social media generates a lot of information that can be used to provide disaster relief organizations with crucial information. The method under discussion is data extraction from social media during disasters, as social media generates a lot of complex data, much of which is not effectively utilized. Therefore, this study offers a technique for obtaining information from Twitter, the primary social media source, to locate and distribute the crucial aid required in emergency and catastrophe situations.Two distinct classification problems are covered in the study. Firstly, preprocessing the classification of images that are relevant to our inquiry is the first task and secondly, classifying four different types of natural disasters, including earthquakes, floods, cyclones, and wildfires. In this study, we employ Convolution Neural Network (CNN) designs along with the help of Transfer Learning (TL) architectures, wherein the model is trained and tested using the secondary data set. Furthermore, real-time tweets and images that are being extracted from Twitter are validated on the trained models and the accuracy is noted. Additional data pretreatment methods like image augmentation have also been used for preprocessing. Transfer Learning, bottleneck feature of inceptionV3 and a fine-tuning model have been included, following the disaster classification through the CNN model as a means to improve our accuracy up to 98.14%, and attaining 0.82 Precision, 0.86 Recall, and 0.84 F1-Score for Cyclone; 0.96 Precision, 0.89 Recall, and 0.92 F1-Score for Earthquake; 0.74 Precision, 0.95 Recall, and 0.83 F1-Score for Flood and 0.97 Precision, 0.96 Recall, and 0.96 F1-Score for Wildfire classification. Disaster can manifest itself in different forms and generally have a negative impact on the biosphere, resulting in loss of property and the environment. This study aims to effectively exercise the power of social media data for disaster management aid.

Developing Nursing Leadership Skills When Working With Disaster Response Programs

As the frequency and intensity of disasters worsen, now is the time for leaders in nursing to respond, hone their leadership skills, and acquire new competencies. The impact of these disasters will wreak havoc on community public health in both the short and long term, and involvement at any level of the four emergency management cycles—mitigation phase, preparedness phase, response phase, and recovery phase—will positively impact community health and enhance nurse leadership skills. Responding to a disaster requires resilience, knowledge of emergency management, willingness to work in austere environments, and transparent communication. Nurse leaders can find these opportunities with nonprofit disaster relief organizations and carry their experience over to leadership roles in health care.

Factors Impacting the Behavioral Intention to Use Social Media for Knowledge Sharing: Insights from Disaster Relief Practitioners

Aim/Purpose: The primary purpose of this study is to investigate the factors that impact the behavioral intention to use social media (SM) for knowledge sharing (KS) in the disaster relief (DR) context. Background: With the continuing growth of SM for KS in the DR environment, disaster relief organizations across the globe have started to realize its importance in streamlining their processes in the post-implementation phase. However, SM-based KS depends on the willingness of members to share their knowledge with others, which is affected by several technological, social, and organizational factors. Methodology: A survey was conducted in Somalia to gather primary data from DR practitioners, using purposive sampling as the technique. The survey collected 214 valid responses, which were then analyzed with the PLS-SEM approach. Contribution: The study contributes to an understanding of the real-life hurdles faced by disaster relief organizations by expanding on the C-TAM-TPB model with the inclusion of top management support, organizational rewards, enjoyment in helping others, knowledge self-efficacy, and interpersonal trust factors. Additionally, it provides useful recommendations to managers of disaster relief organizations on the key factors to consider. Findings: The findings recorded that perceived usefulness, ease of use, top management support, enjoyment in helping others, knowledge self-efficacy, and interpersonal trust were critical factors in determining behavioral intention (BI) to use SM-based KS in the DR context. Furthermore, the mediator variables were attitude, subjective norms, and perceived behavioral control. Recommendations for Practitioners: Based on the research findings, it was determined that management should create different discussion forums among the disaster relief teams to ensure the long-term use of SM-based KS within DR organizations. They should also become involved in the discussions for disaster-related knowledge such as food supplies, shelter, or medical relief that disaster victims need. Disaster relief managers should consider effective and adequate training to enhance individual knowledge and self-efficacy since a lack of training may increase barriers and difficulties in using SM for KS during a DR process. Recommendation for Researchers: The conceptual model, further empirically investigated, can be employed by other developing countries in fostering acceptance of SM for KS during disaster relief operations. Impact on Society: Disaster relief operations can be facilitated using social media by considering the challenges DR practitioners face during emergencies. Future Research: In generalizing this study’s findings, other national or global disaster relief organizations should consider, when applying and testing, the research instruments and proposed model. The researchers may extend this study by collecting data from managers or administrators since they are different types of users of the SM-based KS system.

Filipinos’ intention to participate in 2022 leyte landslide response volunteer opportunities: The role of understanding the 2022 leyte landslide, social capital, altruistic concern, and theory of planned behavior

Leyte is one of the landslide-prone areas in the Philippines. The April 2022 Leyte landslide resulted in severe impacts such as deaths, physical injuries, and damaged infrastructure. Although many organizations spearhead disaster relief operations to help the victims, volunteers' perceptions are overlooked. Thus, the study determined the factors affecting Filipinos' intention to participate in Leyte landslide response volunteer opportunities by extending the Theory of Planned Behavior. A total of 475 valid Filipino respondents participated in the study. The questionnaire comprised fifty (50) measures and seven (7) latent variables. Through structural equation modeling (SEM), eleven (11) hypotheses were formulated and found significant. Understanding the Leyte Landslide had a direct positive effect on social capital and altruistic concern. Both social capital and altruistic concern positively impacted attitude, subjective norm, and perceived behavioral control. Then, attitude, subjective norm, and perceived behavioral control positively influenced intention to volunteer. The findings could be used by stakeholders, the media, government, and disaster relief organizations, to upsurge the volunteerism intention of Filipinos. The stakeholders could adapt the presented strategies and practical implications to establish a more structured system centered on volunteers’ needs. Finally, future researchers could utilize the extended TPB to identify volunteer acts of individuals in other natural disaster-prone regions.

Disaster Information Verification and Validation Application Using Machine Learning

Social media platforms have made it possible for people and organizations to disseminate information to their peers and target markets. Even while most information is shared with the best of intentions, some people utilize social media to further their own agendas. They might publish untrue or inaccurate information in their posts. Before, during, and after disasters and emergencies, social media is rife with rumors, misinformation, and misleading information. These false rumors and information could also make individuals anxious. How to stop the spread of this incorrect information is one of the main problems that public safety authorities and organizations face. DIVVA is a system that, using some provided input text data, evaluates and validates disaster-related information. The system has two tracks: a validation track and a verification track. Verification will classify the textual inputinto categories related to disasters or not related to disasters. The Validation track, on the other hand, will use the official handles of government disaster relief organizations like the NDRF (National Disaster Response Force) to determine whether the event mentioned in the text data actually happened or not before classifying the disaster- related data as real or fake. Therefore, if many individuals receive erroneous information about a calamity, we can utilize our approach to determine if the information is true or false. Our results show that the Bidirectional LSTM model performs well for the tweet classification (i.e. whether the tweets are related to disaster or not) task with 84% accuracy.

The Use of Digital Media and Modes of Communication of Affected People: A Case Study of Earthquakes in East Java, Indonesia

Several earthquake incidents in the East Java province have resulted in numerous disadvantages such as damaged buildings and victims that were physically and emotionally wounded. The problem that has arisen in these situations is how victims of natural disasters often receive late responses and little to no information about relief efforts from authorities. Local governments and disaster relief organizations have not initiated the right method of communication to give important information toward disaster victims when information technologies have developed rapidly. The present study opt for a qualitative research design to conduct the interviews from the victims of disaster living nearby Malang and Blitar regencies of Indonesia. It examines how people living in earthquake-prone regions are communicating during the disaster using smartphones and social networking services. The findings of the research show that people living in the disaster-risk areas use mobile phones, smartphones, and social networking services in their daily life; yet, not many of them using these devices and services to look for information about earthquakes. Although authorities have created digital channels as a source of information, it does not receive significant attention and interest of the people at risk. The problems with uneven digital media literacy and lack of resources remain unresolved. Cooperation between authorities and people is expected, to ensure that the use of existing communication technologies becomes more effective and efficient in earthquake relief efforts and countermeasures.

Evaluating prospects for subseasonal-to-seasonal forecast-based anticipatory action from a global perspective

Globally, the direct cost of natural disasters stands in the hundreds of billions of USD per year, at a time when water resources are under increasing stress and variability. Much of this burden rests on low- and middle-income countries that, despite their relative lack of wealth, exhibit considerable vulnerability such that losses measurably impact GDP. Within these countries, a growing middle class retains much of its wealth in property that may be increasingly exposed, while the few assets the poor may possess are often highly exposed. Vulnerability to extreme events is thus heterogeneous at both the global and subnational level. Moreover, the distribution and predictability of extreme events is also heterogeneous. Disaster managers and relief organizations are increasingly consulting operational climate information services as a way to mitigate the risks of extreme events, but appropriately targeting vulnerable communities remains a challenge. The advent of forecast-based anticipatory action has added to the suite of opportunities—and complexity—of operationalizing such services given varying prediction skill. Forecasts, including those at the subseasonal-to-seasonal (S2S) scale, may allow disaster managers to shift effort and therefore some risk from post-disaster response to pre-disaster preparedness; however, given the recent emergence of such programs, only a few, specific case studies have been evaluated. We therefore conduct a country-scale analysis pairing S2S forecast skill for monthly and seasonal lead times with flood and drought disaster risk to explore the potential for forecast-based anticipatory action programs broadly. To investigate subnational heterogeneity in risk and predictability, we also evaluate focused outcomes for the Greater Horn of Africa and Peru. Results suggest that forecast skill plays a large part in determining suitability for early action, and that skill itself varies considerably by disaster type, lead time, and location. Moreover, the physical and socioeconomic factors of risk can vary greatly between national and subnational levels, such that finer scale evaluations may considerably improve the effectiveness of early action protocols. By considering vulnerability at multiple spatial scales and forecast skill at multiple temporal scales, this analysis provides a first identification of promising locations for anticipatory action protocol development.

Interpretable boosting tree ensemble method for multisource building fire loss prediction

Building fires may cause enormous property loss. Disaster relief organizations and post-disaster recovery efforts benefit from the accurate and interpretable prediction of property loss. To solve this problem, we propose a novel interpretable boosting tree ensemble method (IBTEM), which is of practical significance for providing decision support for dispatching aid and mobilizing recovery resources. First, we fuse multisource datasets including National Fire Incident Reporting System (NFIRS) dataset and National Oceanic and Atmospheric Administration (NOAA) dataset from 2012 to 2016. Second, we construct four variable scenario subsets to select related variables for building fire loss. Third, we adopt Winsorization, logarithmic transformation, recursive feature elimination and weighted voting strategies to create an ensemble of boosting trees. Fourth, we conduct interpretable Shapley additive explanations to analyze the model internal mechanism. The proposed IBTEM is compared with other popular machine learning methods and the experimental results show the IBTEM achieves outstanding superiority. Property value, fire spread and number of stories with minor damage are verified the most effective variables for loss prediction. In conclusion, the IBTEM realizes accurate and interpretable loss prediction of building fires, and assists relevant departments in making disaster relief decisions in a timelier manner.

Temporary Accommodation Facilities for People Affected by an Emergency or Humanitarian Disaster

Aim: This article attempts to provide an insight into the planning and construction of temporary accommodation for those affected by warfare and hu- manitarian disasters. The damage to housing infrastructure is often very extensive and its reconstruction takes up to several years, during which time it is necessary to provide refugees and disaster victims with minimum living conditions until they are able to move into permanent housing facilities. The article further describes the planning process that should be followed before disasters, as well as the types of temporary accommodation and the use of local resources in disaster recovery. Introduction: In recent years, we have seen a significant increase in the occurrence of natural disasters and local armed conflicts. In most cases in- frastructure in disaster areas is severely damaged or completely destroyed. Houses and residential buildings are very vulnerable to damage and are the most visible consequence after disasters. At the same time, for the people affected by these disasters, they are very traumatic experiences. The article describes the key role of temporary housing during the recovery from humanitarian disasters, identifies common problems and provides some suggestions on how to overcome them. Methodology: The paper mainly uses theoretical research methods, including literature analysis and the opinions of international experts who have conducted studies of many humanitarian disasters and warfare. The analysis which was carried out made it possible to establish and present directions for the construction methodology and optimal solutions in the refugee accommodation project. It discusses elements that can be classified as common patterns of temporary accommodation and reviews of both successful and unsuccessful solutions. Conclusions: The article discusses important factors related to humanitarian disaster preparedness and management. In order to assess the prepared- ness of planning and disaster management, the article answers the following questions: what is good planning and management and what is the role of temporary housing? The answer is based on the results of empirical and behavioural research conducted by international experts and UNDRO – the United Nations Disaster Relief Organization and the United Nations High Commissioner for Refugees (UNHCR). Keywords: UNDRO, UNHCR, humanitarian disasters, temporary accommodation Type of article: review article

Personnel needs assessment in times of crisis: a focus on management of disasters

Purpose This study aims to explain the importance of human resources and attempts to identify the competencies required by the personnel involved in disaster management operations. Design/methodology/approach The research uses a qualitative methodology to explore the competencies required by the relief workers using a content analysis approach to analyze the disaster literature and the job advertisements. The data for the content analysis was developed with the help of 23 independent coders, and exploratory inferences were drawn. Findings A detailed review of the literature highlighted the importance of competent personnel in disaster relief organizations. The analysis listed 34 mutually exclusive competencies and their relative importance, which were further divided into four competency clusters. The study also creates a competency dictionary that defines the competencies with the expected behaviors. Practical implications Deploying the right resources in the acute time frame during a disaster event can make a difference, and with lives at stake, such deployment acquires prime importance. In addition to contributing to humanitarian logistics literature, the competency model developed will also help forecast the future requirements and help the organization choose “the right person for the right job.” Originality/value The inferences drawn in the study are based on disaster management areas, unlike earlier research which also considered business logistics research.

Interplay of rumor propagation and clarification on social media during crisis events - A game-theoretic approach

For a rapid dissemination of information during crisis events, official agencies and disaster relief organizations have been utilizing social media platforms, which are susceptible to rumor propagation. To minimize the impact of rumors with limited time and resources, the agencies and social media companies not only need to wisely choose the cases to clarify amongst the numerous cases, but they should also make an informed decision on the timing of clarification. Reacting fast can be misjudged as an obvious best policy as partial/imprecise information may fail to contain the impact of the rumors. On the other hand, investment in terms of time, effort, and money to clarify with more complete information also allows the rumors to spread with their full force during the learning phase, thereby making the process of decision-making very challenging. The objective of this paper is to determine the optimal strategies for the official agencies and social media companies by developing two novel sequential game-theoretic models, namely “Rumor Selection for Clarification” and “Learning for Rumor Clarification”, that can help decide which rumor to clarify and when to clarify, respectively. Results from this study indicate that posting verified information on social media reduces the uncertainties involved in rumor transmission, thereby enabling social media users to make informed decisions on whether to support or oppose the rumor being circulated. This verification needs to be obtained within reasonable limits of time and cost to keep the learning process worthwhile.

A possibilistic mathematical programming model to control the flow of relief commodities in humanitarian supply chains

In emergency situations, disaster relief organizations are faced with the difficult decision of how to allocate scarce resources in an efficient manner in order to provide the best possible relief action. This paper aims to provide an analytical model that will help relief organizations in reducing human suffering following a disaster while maintaining an acceptable level of cost efficiency. A mathematical model is introduced to optimize the relief distribution problem which considers the social cost —the total sum of logistics and deprivation costs. The fuzzy nature of the deprivation cost function is addressed with possibilistic mixed integer programming with fuzzy objectives to reflect variation in deprivation costs perceptions. The model is solved using the Rolling Horizon method in a sequence of iterations. In each iteration, part of the planning horizon is modeled in detail and the rest of the time horizon is represented in an aggregated manner. The model is tested both empirically and on a case study of internal displacement in northwest Syria. Computational results showed that considering the demographic structure in affected areas and reflecting it to the deprivation cost function helped to reach better prioritization in distribution of commodities. The rolling horizon methodology is also found to be efficient in solving large scale instances and in capturing the dynamic changes in demand and supply parameters.

A hybrid indicator many‐objective optimization algorithm for the selection and delivery of disaster relief materials problem

AbstractTraditional methods fail to comprehensively consider the selection and delivery of disaster relief materials (SDDM) problem. To solve the problem, the SDDM will be comprehensively considered in this article, which will be built into a many‐objective disaster relief materials selection and delivery model with four objectives to be optimized in the last kilometer of the disaster relief organization. Meanwhile, a many‐objective optimization algorithm is designed to solve the proposed disaster relief materials model by employing the different excellent indicators. Two extensive simulation experiments are carried out. On the one hand, the performance of the designed many‐objective is tested on DTLZ and WFG benchmark functions. On the other hand, the proposed many‐objective model is solved by employing the designed algorithm. In addition, the simulation result shows that the designed algorithm is effective in solving the SDDMs problem.

The impact of social media on disaster relief effort – recovery coordination for Hurricane Harvey

PurposeThis study aims to investigate patterns of social media posting occurring during the initial post-disaster recovery period. In addition, the study investigates the antecedents of user engagement with Twitter posts or “tweets.”Design/methodology/approachThis study uses Hurricane Harvey as the research focus because of its $125bn in damage and the recovery effort associated with it. Analyzing nearly 38,000 tweets related to 2017’s Hurricane Harvey and specifically focuses on rescuers who became to be known as the “Cajun Navy.”FindingsThe popularity of the tweet significantly influences social media engagement; disaster relief organizations first should seek out alliances and partnerships with those who already are well-known or influential in social media. In addition, by regularly monitoring and participating in social media, such as encouraging retweets and mentions even when not responding to a disaster, agencies will gain social media followers and influence when these are most needed.Research limitations/implicationsBy analyzing patterns of social media posts during and after Hurricane Harvey, this study attempts to quantify social media’s effectiveness during this disaster, in particular with a focus on what characteristics of Harvey-related Twitter posts most influenced user engagement.Practical implicationsFindings underscore the need for agencies to strengthen their social media presence and use it in all three phases of disaster conceptualization. Beyond managing communication with other agencies and the community, which alone is critical for an agency’s impact, social media offers the world as a potential audience for agencies that can deftly leverage it.Originality/valueSocial media has revolutionized not only communication but also inter-connectivity as people have normalized its use. Its ubiquity and efficiency have seen its incorporation into the critical area of emergency management, both during and after disasters. The main objective of this paper is to investigate and evaluate the usefulness of social media in disaster recovery efforts, focusing on its advantages in relation to a recent disaster event.

Control Effect Analysis of a Bridge under Control of Neutral Equilibrium Mechanisms (NEMs) as Multiple Virtual Piers

When natural disasters occur, real-time disaster relief operations need to be implemented. But if disaster relief transportation is interrupted, these operations will be severely hampered. Emergency relief bridges must be erected so that people, materials and machinery can reach the disaster area smoothly. Therefore, one indispensable tool of disaster relief organizations is the light bridge. The design of such a lightweight bridge is controlled by deformation, not strength, so the span of the bridge is limited. Thus, this study proposes a bridge with multiple NEMs acting as multiple virtual piers of the bridge and an active structural control method was adopted to expand the span of the light bridge. The experimental results showed that, for the maximum vertical displacements at 1/3 and 2/3 distances from the left side of the bridge under uncontrolled and controlled conditions, the displacement reduction rates of the bridge were 96.20% and 98.42% for the outbound trip and 93.32% and 98.47% for the return trip, respectively. For the displacement control at the midpoint of the bridge under uncontrolled and controlled conditions, the displacement reduction rates of the outbound and return trips were 96.77% and 96.62%, respectively. The images from the experiment also showed that the cantilever arm of each NEM could be rotated by the PID controller to the appropriate angle to provide sufficient upward force to balance the vertical displacement changes based on the position of the moving load. Thus, zero-displacement control of a bridge can be achieved, and the control effect is quite significant.