Construction Tasks Research Articles

Exploratory study on time-delayed excavator teleoperation in virtual lunar construction simulation: Task performance and operator behavior

Building sustainable habitats on the moon has been planned for decades. However, applying fully automated construction systems is still challenging in altered environments. Teleoperation, which is the remote control of the machine, can serve as an intermediate phase before achieving fully autonomous systems. Since the teleoperation between operators on the earth-ground and robots on the lunar surface introduces inevitable communication time delays under a deep space network system, it is important to understand its impact on task performance and operator behaviors in teleoperated construction tasks. This paper develops a simulated lunar environment for excavator teleoperation systems in virtual reality to examine task performance and operator behaviors in time delay conditions. The outcomes indicate that time delays significantly degrade task performance, and the operators modify their control strategies to cope with the time delay conditions. The findings will contribute to understanding human behaviors in time-delayed teleoperation of lunar construction tasks.

Head kinematics of human subjects during laboratory-induced ladder falls to the ground

Introduction: Fall-induced traumatic brain injury (TBI) is considered one of the most serious occupational injuries in construction. Given the frequency of falls from ladders, knowledge of head kinematics during ladder falls to the ground may help inform any potential improvement to construction safety helmet design and improve their protection against head injury. Therefore, the goal of this descriptive study was to measure head kinematics during laboratory-induced ladder falls to the ground. Method: Eighteen young adults wearing a hockey helmet simulated construction tasks that challenged their balance while standing on stepladders and an extension ladder with their feet at heights up to 1.8 m above padding covering the ground. Falls onto the padding occurred spontaneously or were induced by an investigator nudging the ladder to simulate ladder movement resulting from the ground shifting. Optoelectronic motion capture was used to capture head kinematics up to the instant immediately before head impact. Results: Of 115 total falls, 15 involved head impact with the padding and were analyzed. Head impact during all 15 of these falls occurred on the back of the head. Immediately before impact with the padding, head vertical velocity ranged from 0.42 to 3.88 m/s and head angular velocity about a medial–lateral axis ranged from 60.1 to 1215.5 deg/s. Conclusions: These data can be used with computer simulations or headform impact testing to estimate true head impact kinematics, or to inform future versions of construction safety helmet testing standards. Practical applications: This is the first study we are aware of to capture head kinematics of human subjects during ladder falls to the ground. These results have the potential to inform future versions of construction safety helmet testing standards and contribute to improved helmet design for protection against fall-induced head injury.

Decision Support System (DSS) for Improving Production Ergonomics in the Construction Sector

Ergonomics is essential to improving workplace safety and efficiency by reducing the risks associated with physical tasks. This study presents a decision support system (DSS) aimed at enhancing production ergonomics in the construction sector through an analysis of high-risk postures. Using the Ovako Work Posture Analysis System (OWAS), the Revised NIOSH Lifting Equation (NIOSH equation) and Rapid Entire Body Assessment (REBA), the DSS identifies ergonomic risks by assessing body postures across common construction tasks. Three specific postures—X, Y and Z—were selected to represent typical construction activities, including lifting, squatting and repetitive tool use. Posture X, involving a forward-leaning stance with arms above the shoulders and a 25 kg load, was identified as critical, yielding the highest OWAS and NIOSH values, thus indicating an immediate need for corrective action to mitigate risks of musculoskeletal injuries. The DSS provides recommendations for workplace adjustments and posture improvements, demonstrating a robust framework that can be adapted to other postures and industries. Future developments may include application to other postures and sectors, as well as the use of artificial intelligence to support ongoing ergonomic assessments, offering a promising solution to enhance Occupational Safety and Health policies.

A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes

A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes

Embracing Levin’s Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design Through STS Approaches

AbstractThis paper investigates the synergy between Levin’s theories on technology transfer as a socio-technical learning and developmental process (TLD process), and what we learnt about socio-technical systems (STS) theories in a case study developing human robot solutions for the construction sector. Levin’s extensive work highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning and highlights the importance of incorporating cultural knowledge and skills into the technological transfer process. Contemporary STS views developed through our own work are introduced to complement and extend Levin’s theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs. To illustrate the synergies and potential challenges from Levin’s theories of technology transfer with contemporary STS concepts, we use a qualitative study of a unique case about the design and development of human-robot teams (HRTs) for construction tasks. Our findings reveal that while Levin’s theories provide a valuable foundation for understanding technology transfer and organizational change, contemporary socio-technical systems face unique challenges in the context of AI-driven human-robot teams, where intelligent robots also contribute to the socio-technical learning process. Moreover, the rapidly evolving nature of technology and innovations could exponentially impact on multidisciplinary design teams, stakeholder participation and inter-organizational dynamics. The discussions suggest an extension of co-generative learning to incorporate ‘collaborative intelligence’ between human-robot teams enabled by artificial intelligence (AI). Consequently, we suggest that Levin’s theories of technology transfer, developed before the rapid application of AI, may not have fully considered further social challenges caused by the introduction of autonomous systems such as AI-driven HRT systems. We extend Levin’s important work by suggesting that addressing such challenges requires ongoing dialogue and collaboration among researchers, practitioners, and policymakers with different disciplinary backgrounds to develop robust and reliable socio-technical systems frameworks to navigate the complexities of robotics and AI in today’s rapidly evolving technological landscape.

Optimized Factor Graph for Tightly-Coupled LiDAR/IMU Localization in Underground Parking Garages

Abstract. To address the challenge of intelligent vehicle localization in underground parking structures due to the loss of GNSS signals, this paper introduces a method to address this issue by developing a novel localization framework known as GF-LIO, which denotes a tightly-coupled fusion of LiDAR and IMU data, innovatively combining the Interactive Extended State Kalman Filter (IESKF) with a factor graph to enhance the localization process and solve the problem of GNSS signal loss in underground parking lots. The GF-LIO model commences with a strategic feature selection process, facilitated by a greedy algorithm that prioritizes environmental cues within the point cloud data. This method effectively filters out redundant features, thereby enhancing the saliency of retained features and subsequently improving the robustness of the localization process. Following feature selection, the model integrates LiDAR and IMU measurements utilizing the IESKF algorithm, ensuring a cohesive fusion of sensor data and bolstering attitude estimation accuracy. The culmination of the GF-LIO framework involves factor graph optimization, a sophisticated technique that synthesizes LiDAR odometry, IMU pre-integration factors, and loop closure detection factors. This optimization step enhances the overall precision and consistency of the localization process, resulting in superior performance compared to existing methodologies. Experimental evaluations conducted within underground parking environments corroborate the efficacy of the GF-LIO model. Comparative analyses against established approaches such as A-LOAM, LeGO-LOAM, LIO-LOAM, and FAST-LIO demonstrate a notable performance improvement exceeding 12.53%. The proposed model adeptly integrates domain-specific environmental characteristics with multi-sensor data, thereby facilitating precise localization and map construction tasks for intelligent vehicle navigating within the intricate confines of subterranean parking structures.

Comparative Analysis of Single and Multiagent Large Language Model Architectures for Domain-Specific Tasks in Well Construction

Summary This paper explores the application of large language models (LLMs) in the oil and gas (O&G) sector, specifically within well construction and maintenance tasks. The study evaluates the performances of a single agent and a multiagent LLM-based architecture in processing different tasks, offering a comparative perspective on their accuracy and the cost implications of their implementation. The results indicate that multiagent systems offer improved performance in question and answer (Q&A) tasks, with a truthfulness measure 28% higher than single-agent systems but at a higher financial cost. Specifically, the multiagent architecture incurs costs that are, on average, 3.7 times higher than those of the single-agent setup due to the increased number of tokens processed. Conversely, single-agent systems excel in Text-to-SQL (structured query language) tasks, particularly when using the Generative Pre-Trained Transformer 4 (GPT-4), achieving a 15% higher score compared to multiagent configurations, suggesting that simpler architectures can sometimes outpace complexity. The novelty of this work lies in its original examination of the specific challenges presented by the complex, technical, unstructured data inherent in well construction operations, contributing to strategic planning for adopting generative artificial intelligence (AI) (Gen-AI) applications and providing a basis for optimizing solutions against economic and technological parameters.

Competencies for Human-Robot Collaboration in the Construction Industry – Academia’s Perspective

ABSTRACT This paper investigates the perceptions of Construction Engineering and Management instructors regarding the competencies for Human-Robot Collaboration (HRC) in construction to determine knowledge areas, skills, and abilities prioritized for facilitating effective collaboration between humans and robots in the industry. A two-round Delphi study was employed to evaluate the perceptions of construction instructors regarding HRC competencies. This study’s findings revealed that human-robot collaboration knowledge areas prioritized by the instructors include HRC ethics and regulation, robot anatomy and technical specifications, construction robot applications, sensors, and task planning. The instructors prioritized skills such as task planning, application of machine learning algorithms, safety management, human-robot interface proficiency, and effective communication. HRC abilities prioritized include decision-making, continuous learning, critical thinking, attention to detail, analytical aptitude, and adaptability. This research established the competencies prioritized by the instructors for implementing HRC in the construction industry and recommended future research directions.

The Development of the Mental Timeline is Related to Temporal Memory

ABSTRACT Although we cannot see or touch time, across many cultures, we use spatial representations to think about this abstract concept. Spatial representations of time are thought to support temporal concepts that might otherwise be difficult to represent and reason about, such as the temporal component of episodic memory. One common form of spatially representing time is the mental timeline, which is a linear projection of time onto space. Adults and older children from many cultures spontaneously activate the mental timeline when remembering the order of events. However, the mental timeline develops slowly throughout early childhood as children gain increasing experience with formal schooling and cultural artifacts. Here, we explored how individual differences in the development of American children’s mental timelines relate to their memory for temporal order (N = 96, M age = 6.22 years). We first tested children’s memory for the order and location of events through a memory task involving short videos. We then tested children’s spontaneous spatial representations of temporal order using an open-ended timeline construction task. The linearity of children’s timeline arrangements predicted their memory for temporal order but not memory for locations: children who spontaneously represented time linearly had significantly better temporal memory performance than children who represented time nonlinearly, but there was no difference in location memory performance. These data provide preliminary evidence that the development of the mental timeline may support the development of temporal memory.

Specific and shared cognitive predictors of drawing and block building in typically developing children.

Spatial construction is a complex ability involving attention, global/local visual processing, mental representation, visuo-motor coordination and, to varying extent, working memory and executive functions, and verbal abilities. In developmental neuropsychology, little attention has been paid to comprehend whether and to what extent the above cognitive processes are involved in two main spatial construction tasks, that is drawing and block building. We used path analysis to test shared and specific effects of verbal and spatial working memory, spatial attention, inhibition, verbal abilities (vocabulary and naming), figure disembedding, mental rotation, and visual-motor coordination, as well as of demographics (sex, age and socio-economic status), on two classical drawing (Rey-Osterrieth Complex Figure; ROCF) and block building (Block design; BD) tasks in a sample of 195 typically developing children (age range: 7-11 years). Figure disembedding and visuo-motor coordination were the only shared predictors of both spatial construction tasks. Moreover, ROCF score was directly related with spatial attention and inhibition, while BD score was directly related with sex, vocabulary, mental rotation and backward spatial working memory. These findings distinguish between abilities involved in spatial construction regardless of the type of task and those specifically related to ROCF or Block Design, thus providing clues relevant to neuropsychological assessment and intervention in children with spatial construction disorders.

Auto-weighted Graph Reconstruction for efficient ensemble clustering

Ensemble clustering (EC) is an important field in data clustering, which aims to combine multiple base clustering results of a given dataset into a single consensus result. Previous studies have shown that one of the effective methods is to convert the base clustering results into a graph partitioning problem. Existing spectral ensemble clustering methods typically obtain a spectral embedding matrix for the graph and then perform k-means, separating the graph construction and clustering tasks into two distinct stages. To address these challenges, we propose a novel and efficient ensemble clustering method named Auto-weighted Graph Reconstruction for efficient ensemble clustering. This method integrates weighted base clusters, consensus distance graph construction, and clustering into a unified framework. A consensus distance graph is constructed, from which a stable and discrete label matrix is derived using spectral clustering. An effective optimization algorithm is then employed to address the resulting problem. Finally, the experimental results on eight real-world datasets verify the effectiveness and superiority of the proposed method.

L-FNNG: Accelerating Large-Scale KNN Graph Construction on CPU-FPGA Heterogeneous Platform

Due to the high complexity of constructing exact k -nearest neighbor graphs, approximate construction has become a popular research topic. The NN-Descent algorithm is one of the representative in-memory algorithms. To effectively handle large datasets, existing state-of-the-art solutions combine the divide-and-conquer approach and the NN-Descent algorithm, where large datasets are divided into multiple partitions, and a subgraph is constructed for each partition before all the subgraphs are merged, reducing the memory pressure significantly. However, such solutions fail to address inefficiencies in large-scale k -nearest neighbor graph construction. In this paper, we propose L-FNNG, a novel solution for accelerating large-scale k -nearest neighbor graph construction on CPU-FPGA heterogeneous platform. The CPU is responsible for dividing data and determining the order of partition processing, while the FPGA executes all construction tasks to utilize the acceleration capability fully. To accelerate the execution of construction tasks, we design an efficient FPGA accelerator, which includes the Block-based Scheduling (BS) and Useless Computation Aborting (UCA) techniques to address the problems of memory access and computation in the NN-Descent algorithm. We also propose an efficient scheduling strategy that includes a KD-tree-based data partitioning method and a hierarchical processing method to address scheduling inefficiency. We evaluate L-FNNG on a Xilinx Alveo U280 board hosted by a 64-core Xeon server. On multiple large-scale datasets, L-FNNG achieves, on average, 2.3× construction speedup over the state-of-the-art GPU-based solution.

NEUROPSYCHOLOGICAL ASPECTS OF EXECUTIVE FUNCTIONS IN THE SPATIAL CONSTRUCTION OF COLOR MODELS IN STUDENTS WITH AND WITHOUT LEARNING DIFFICULTIES

The effectiveness of the spatial construction of models is directly related to the degree of internal spatio-temporal structuring and organization which, in turn, depends on the degree of the functional optimization of the subjects of the action. The objective of this study is to detect neuropsychological differences in executive functions, between students with and without learning difficulties, in terms of attempts and time spent performing spatial construction tasks. The research hypothesis states that there are differences in the number of attempts and time spent when performing spatial tasks, with greater expressiveness in students with learning difficulties. In methodology, the resource focused on the neuropsychological instrument of spatial construction used colored models based on the manipulation of four Kohs cubes; as well as neuropsychological observation and psychological interview. The results show that at most ages the tendency is for students with learning difficulties to make a greater number of attempts and spend more time performing tasks related to spatial construction tests, compared to students without learning difficulties. In conclusion, it is inferred that students with learning difficulties tend to present internal spatio-temporal structuring and organization deficits, which result from deficits in the operability of executive functions.

Construction Tasks for Children With Blindness: Analyzing the Completion Ratio of Six Puzzles

Introduction: There are major differences when children with blindness perform construction tasks using haptics, without relying on sight, compared to when children with typical vision perform them through sight. When using sight, in all of the processes, the overall image can be grasped simultaneously while executing the task. When using haptics, the construction must be made by touching each piece sequentially and successively. In this study, framed block puzzle construction tasks that can be implemented by haptic means are developed with the objective of considering the order of difficulty of the task. Methods: In all, six framed block puzzle construction tasks were performed by 39 children with blindness, and the analysis was conducted from the perspective of the task-completion ratio. These six tasks were established on the basis of factors such as wood panel division and dimension, symmetry and asymmetry, and the number of block patterns. Results: A significant difference was observed in the completion ratio between one- and two-dimensional tasks, between two-dimensional symmetrical and asymmetrical tasks, and between two-dimensional asymmetrical tasks with multiple and single correct patterns. Discussion: This study clarified that the task order of difficulty is influenced by the factors of division and dimension, symmetry and asymmetry, and number of block patterns. Thus, we identified a task order wherein difficulty increases in the order of one- and two-dimensional symmetrical tasks and two-dimensional asymmetrical tasks with multiple and single correct patterns. Implications for Practitioners: Based on these hypotheses, providing instructions to children with blindness, in the order of simple to difficult tasks, is vital after ascertaining each child's level of capacity for spatial cognition.

Do psychological traits influence the perceived usefulness of rule recommendations in configuration tasks?

ABSTRACT In this paper, we describe an empirical evaluation of the user's perceived usefulness of recommendations for configuration tasks in a smart home scenario. Our results suggest that while overall recommendations help improve the performance in the task, the psychological traits of Self-efficacy and Need for Cognition play an important role in determining the perceived usefulness of the recommendations. These effects are somehow different from those reported in other studies where recommendations are provided for a choice task rather than a constructive task. Thus, our results offer evidence regarding the importance of incorporating the evaluation of personality traits into the design of configuration constructive tasks in order to make informed decisions on whether and how to provide users with recommendations. Additionally, they show that it is crucial to take into account the nature of the task and, where possible, individual competencies.

Path planning for Multi-USV target coverage in complex environments

This research introduces a path planning methodology aimed at coordinating multiple unmanned surface vehicles (USVs) to accomplish target coverage task in obstacle-rich environments. Leveraging the locking sweeping method (LSM), our proposed method constructs task target point distance fields that integrates environmental constraints, alongside a task target point distance matrix. During the task allocation phase, we design a cost assessment function to assess the generated allocation solutions. And a greedy allocation strategy is employed to determine the optimal number of USVs required for mission completion, ensuring evenly distribution of target task points among them. Subsequently, we improve the ant colony optimization (ACO) method by redesigning the heuristic function and pheromone update rules, considering environmental constraints and task execution sequence constraints. This refinement facilitates the generation of optimized task execution sequences and safe navigation paths in obstacle environments. The effectiveness of the proposed method is validated through multiple sets of simulation experiments and compared with existing methods. The results demonstrate the practicality and efficacy of the method in addressing the challenges of USVs target coverage task in obstacle environments.

Beauty Is the Gravitas Amoris: A Trinitarian Correlation of Beauty and Love

There have been many attempts to give a precise definition of beauty. In this article, I join a tradition of trinitarian reflection on beauty and attempt to add further clarity to a trinitarian definition of beauty. Beauty, in my construal, is the gravitas amoris of the triune being of love—it is, in other words, the objective attractional “force” or “weightiness” exerted by triune love on the soul by which one, if she yields to triune love, experiences the bliss and delight of triune love and is thereby drawn into deifying union. This article will proceed as follows: First, I will survey the relationship between beauty and love found in the classic philosophical tradition represented by Plato, Aristotle, and Plotinus. Then, I will survey this beauty–love relation in Christian thought from Augustine, Aquinas, Ficino, Edwards, and von Hildebrand. These figures will provide supporting evidence for my construal of beauty. I then turn to the constructive task. I argue that there is an abductive fit between the features of beauty and the nature of the Trinity. In light of this fit and the sources of traditional thought above, beauty is best thought of as the gravitas amoris—the weighty impression of God’s triune love in created things that draws one into the triune life and is experienced as delight when perceived. I conclude by answering objections and reflecting on how this account reframes the transcendentals and coordinates the experience of beauty with Christian love.

Exploration of the Talent Cultivation Model for the Integration of Industry and Education in University Asset Evaluation

During the 14th Five Year Plan period, the main task of talent team construction in China’s asset appraisal industry was to develop innovative talent training models. Therefore, this article focuses on the talent cultivation model of integrating industry and education in asset evaluation in universities, systematically summarizes the theoretical and practical significance of research on asset evaluation talent cultivation models in universities, and explores the construction measures of asset evaluation talent cultivation models based on the integration of industry and education that meet social needs and the development of the times [1]. At the same time, the strategy of constructing a deep integration talent training system was explored, guided by the integration of industry and education, to cultivate asset evaluation composite talents with strong practical skills. The aim is to provide a reference for improving the quality of asset evaluation professionals in China and promoting the development of asset evaluation talent training models.

StaRS: Learning a Stable Representation Space for Continual Relation Classification.

Relation classification (RC) aims to detect the semantic relation between two annotated entities in a piece of sentence, serving as an essential task in automatic knowledge graph construction. Due to the emergence of new relations, there is a recent trend to train RC models in continual settings. To overcome the catastrophic forgetting problem in continual learning, existing research is devoted in a two-stage training paradigm, fast adaptation to novel relations, and memory replay for all historical relations. These memory-replay-based methods explore different techniques to mitigate the forgetting problem of continual RC (CRC) models during the memory replay stage. However, we find that the representation space undergoes distortion due to the incoming of fresh relations in the fast adaptation phase. To address this issue, we propose using a knowledge distillation strategy and designing a margin loss, aiming to maintain the stability of the RC model during adaptation to new relations. In addition, in the second stage, with a limited number of typical memory instances available, we introduce a self-contrastive learning objective to facilitate learning a balanced decision boundary for RC. Through training in two stages, our objective is to acquire a stable representation space to encode instances for CRC. We experimentally demonstrate the superiority of our model over competing methods in various settings, and the results suggest that our tailored designs can achieve better performance in CRC.

A Study on the Path to Enhance the Curriculum Ability of Vocational College Teachers Under the Background of New Liberal Arts Construction

In the context of the construction of the new humanities, improving teachers' curriculum abilities is an essential aspect of promoting high-quality teaching development in vocational colleges. Currently, vocational colleges should focus on the goals and tasks of new liberal arts construction, innovate teacher training mechanisms, take practical and effective measures, promote teacher curriculum capacity building, and enhance teachers' ability and ability to cultivate morality and cultivate talents, further promoting the overall improvement of school teaching quality.