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Navajo Textilesprovides a nuanced account the Navajo weavings in the Crane Collection at the Denver Museum of Nature & Science-one of the largest collections of Navajo textiles in the world. Bringing together the work of anthropologists and indigenous artists, the book explores the Navajo rug trade in the mid-nineteenth century and changes in the Navajo textile market while highlighting the museum's important, though still relatively unknown, collection of Navajo textiles. In this unique collaboration among anthropologists, museums, and Navajo weavers, the authors provide a narrative of the acquisition of the Crane Collection and a history of Navajo weaving. Personal reflections and insights from foremost Navajo weavers D. Y. Begay and Lynda Teller Pete are also featured, and more than one hundred stunning full-color photographs of the textiles in the collection are accompanied by technical information about the materials and techniques used in their creation. An introduction by Ann Lane Hedlund documents the growing collaboration between Navajo weavers and museums in Navajo textile research. The legacy of Navajo weaving is complex and intertwined with the history of the Diné themselves.Navajo Textilesmakes the history and practice of Navajo weaving accessible to an audience of scholars and laypeople both within and outside the Diné community.

Declassified documents from the \"Crane Collection\" at the National Archives (USA) reveal much of the cryptanalytical collaboration that defeated the German Naval Enigma machine. As researchers continue to work through these papers, new light is shed on that relationship. In May, 2002 a manuscript, typed and handwritten, by Alan M. Turing was found by the author in the \"Crane Collection\". Written at the time of his United States visit during the winter of 1942-1943, it reflects Government Code and Cypher School (GC&CS) interests and skepticism regarding the US Naval Intelligence (OP-20-G) effort to independently design and construct its own rapid analytical machines (RAMs).

Tower cranes play a crucial role in the construction industry, facilitating the vertical and horizontal movement of materials and aiding in building construction, especially for high-rise structures. However, tower crane accidents can lead to severe consequences, highlighting the importance of effective safety management and inspection. This paper presents a novel approach to tower crane safety inspections using Unmanned Aerial Vehicles (UAVs) equipped with high-definition cameras and an intelligent inspection APP system. The system utilizes real-time kinematic (RTK) positioning and digital image processing to perform efficient and comprehensive inspections, reducing the reliance on manual labor and associated risks. A case study demonstrated the method’s practicality and effectiveness, with the UAV inspections capable of identifying 76.3% of major hazards, 64.8% of significant hazards, and 76.2% of general hazards within a 30-minute timeframe. Preliminary identification rates were also promising. Despite the initial requirement for manual drone piloting and the current manual review of images, the approach shows significant potential for enhancing safety in the construction industry. Future work will focus on integrating AI for hazard recognition and automating the inspection process further. The proposed method marks a step forward in tower crane safety management, offering a more efficient and accurate alternative to traditional inspection methods.

PurposeThis research is aimed at predicting tower crane accident phases with incomplete data.Design/methodology/approachThe tower crane accidents are collected for prediction model training. Random forest (RF) is used to conduct prediction. When there are missing values in the new inputs, they should be filled in advance. Nevertheless, it is difficult to collect complete data on construction site. Thus, the authors use multiple imputation (MI) method to improve RF. Finally the prediction model is applied to a case study.FindingsThe results show that multiple imputation RF (MIRF) can effectively predict tower crane accident when the data are incomplete. This research provides the importance rank of tower crane safety factors. The critical factors should be focused on site, because the missing data affect the prediction results seriously. Also the value of critical factors influences the safety of tower crane.Practical implicationThis research promotes the application of machine learning methods for accident prediction in actual projects. According to the onsite data, the authors can predict the accident phase of tower crane. The results can be used for tower crane accident prevention.Originality/valuePrevious studies have seldom predicted tower crane accidents, especially the phase of accident. This research uses tower crane data collected on site to predict the phase of the tower crane accident. The incomplete data collection is considered in this research according to the actual situation.

Container yard congestion can become a bottleneck in port logistics and result in accidents. Therefore, transfer cranes, which were previously operated manually, are being automated to increase their work efficiency. Moreover, LiDAR is used for recognizing obstacles. However, LiDAR cannot distinguish obstacle types; thus, cranes must move slowly in the risk area, regardless of the obstacle, which reduces their work efficiency. In this study, a novel method for recognizing the position and class of trained and untrained obstacles around a crane using cameras installed on the crane was proposed. First, a semantic segmentation model, which was trained on images of obstacles and the ground, recognizes the obstacles in the camera images. Then, an image filter extracts the obstacle boundaries from the segmented image. Finally, the coordinate mapping table converts the obstacle boundaries in the image coordinate system to the real-world coordinate system. Estimating the distance of a truck with our method resulted in 32 cm error at a distance of 5 m and in 125 cm error at a distance of 30 m. The error of the proposed method is large compared with that of LiDAR; however, it is acceptable because vehicles in ports move at low speeds, and the error decreases as obstacles move closer.

Accurately predicting the payload movement and ensuring efficient control during dynamic tower crane operations are crucial for crane safety, including the ability to predict payload mass within a safe or normal range. This research utilizes deep learning to accurately predict the normal and abnormal payload movement of tower cranes. A scaled-down tower crane prototype with a systematic data acquisition system is built to perform experiments and data collection. The data related to 12 test case scenarios are gathered, and each test case represents a specific combination of hoisting and slewing motion and payload mass to counterweight ratio, defining tower crane operational variations. This comprehensive data is investigated using a novel attention-based deep neural network with Tree-Structured Parzen Estimator optimization (TPE-AttDNN). The proposed TPE-AttDNN achieved a prediction accuracy of 0.95 with a false positive rate of 0.08. These results clearly demonstrate the effectiveness of the proposed model in accurately predicting the tower crane payload moving condition. To ensure a more reliable performance assessment of the proposed AttDNN, we carried out ablation experiments that highlighted the significance of the model’s individual components.

Due to their unique structural design, portal cranes have been extensively utilized in bulk cargo and container terminals. The bearing fault of their drive motors is a critical issue that significantly impacts their operational efficiency. Moreover, the problem of imbalanced fault samples has a more pronounced influence on the application of novel fault diagnosis methods. To address this, the paper presents a new method called bidirectional gated recurrent domain adversarial transfer learning (BRDATL), specifically designed for imbalanced samples from portal cranes’ drive motor bearings. Initially, a bidirectional gated recurrent unit (Bi-GRU) is used as a feature extractor within the network to comprehensively extract features from both source and target domains. Building on this, a new Correlation Maximum Mean Discrepancy (CAMMD) method, integrating both Correlation Alignment (CORAL) and Maximum Mean Discrepancy (MMD), is proposed to guide the feature generator in providing domain-invariant features. Considering the real-time data characteristics of portal crane drive motor bearings, we adjusted the CWRU and XJTU-SY bearing datasets and conducted comparative experiments. The experimental results show that the accuracy of the proposed method is up to 99.5%, which is obviously higher than other methods. The presented fault diagnosis model provides a practical and theoretical framework for diagnosing faults in portal cranes’ field operation environments.

Tower crane safety governance is an important issue related to the sustainable development of China’s construction industry. The complex collaborative relationship among stakeholders determines the efficiency of tower crane safety governance. From the perspective of social networks, this study constructs a collaborative governance structure model of tower crane safety from four dimensions, i.e., transaction, supervision, dependency, and communication, and analyzes the structural characteristics of tower crane safety collaborative governance and the mutual relationship among stakeholders. The results show that the tower crane safety governance process has a strong collaborative effect, but that collaboration in terms of supervision and communication among stakeholders is currently poor. The tower crane property owner occupies the core position, so their decisions have a great impact on tower crane safety. The power of the government is too large, and the power of supervision is too small, which affects the collaboration enthusiasm of other stakeholders, thus reducing the overall collaboration efficiency. The findings provide theoretical support for tower crane safety management in the construction industry in China. The social network perspective presented in this study can be applied to clarify relationships among stakeholders in other construction safety governance fields.

Sensing technologies demonstrate promising potential in providing the construction industry with a safe, productive, and high-quality process. The majority of sensing technologies in the construction research area have been focused on construction automation research in prefabrication, on-site operation, and logistics. However, most of these technologies are either not implemented in real construction projects or are at the very early stages in practice. The corresponding applications are far behind, even in extensively researched aspects such as Radio Frequency Identification, ultra-wideband technology, and Fiber Optic Sensing technology. This review systematically investigates the current status of sensing technologies in construction from 187 articles and explores the reasons responsible for their slow adoption from 69 articles. First, this paper identifies common sensing technologies and investigates their implementation extent. Second, contributions and limitations of sensing technologies are elaborated to understand the current status. Third, key factors influencing the adoption of sensing technologies are extracted from construction stakeholders’ experience. Demand towards sensing technologies, benefits and suitability of them, and barriers to their adoption are reviewed. Lastly, the governance framework is determined as the research tendency facilitating sensing technologies adoption. This paper provides a theoretical basis for the governance framework development. It will promote the sensing technologies adoption and improve construction performance including safety, productivity, and quality.

PurposeThis paper examines how the altering nature of planning decisions affects operational efficiency in seaport container terminals. The uncertainty and the role of the planner were investigated considering the dynamic integrated planning function of the quay to yard interface.Design/methodology/approachA system dynamics model has been built to illustrate the integrated dynamic environment. Data collection was conducted at a leading container terminal at a hub port. The model was simulated for different scenarios to derive findings.FindingsThe planner has been identified as the agent who makes alterations between the initial operational plan and the actual plan. The initial plan remains uncertain even when there is no impact from crane breakdowns, requiring a significant number of alterations to be made. The planner who had worked on the yard plan had altered (approximately 45%) the initial plan than the alterations done by the planner who had worked on the vessel plan. As a result, the feedback loop that is created by the remaining moves at each hourly operation influences the upcoming operation as much as crane breakdowns influence.Originality/value The uncertainty and the role of the planner were investigated considering the dynamic integrated planning function of the quay to yard interface. The findings of this study are significant since terminal efficiency is examined considering the quayside and landside as an integrated system.