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The purpose of this paper is to actively calibrate power density to match the application requirements with as small an actuator as possible. So, this paper introduces shape memory alloy to design variable stiffness elements. Meanwhile, the purpose of this paper is also to solve the problem of not being able to install sensors on shape memory alloy due to volume limitations.

This paper introduces the design, modeling and control process for a variable stiffness passive ankle exoskeleton, adjusting joint stiffness using shape memory alloy (SMA). This innovative exoskeleton aids the human ankle by adapting the precompression of elastic components by SMA, thereby adjusting the ankle exoskeleton’s integral stiffness. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function.

Using SMA as the driving force for stiffness modification in passive exoskeletons introduces several distinct advantages, inclusive of high energy density, programmability, rapid response time and simplified structural design. In the course of experimental validation, this ankle exoskeleton, endowed with variable stiffness, proficiently executed actions like squatting and walking and it can effectively increase the joint stiffness by 0.2 Nm/Deg.

The contribution of this paper is to introduce SMA to adjust the stiffness to actively calibrate power density to match the application requirements. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function.

A biomechanical design method of lightweight full contacted insole based on structural anisotropy bespoke (SAB) is proposed, which can better redistribute the stress distribution of SAB designed personalized insole.

The reconstructed joint biomechanics are simulated using finite element analysis (FEA) to develop a lightweight full contact insole. Innovatively, the anisotropic properties of the triply periodic minimal surface (TPMS) structure, which contribute to reducing insole weight, are considered to optimize stress distribution. Additionally, porosity and manufacturing time are included as design objectives. To validate the lightweight insole design, FEA is employed to simulate the stress distribution of the ergonomic insole, which can be fabricated by additive manufacturing (AM) with TPU.

With a little 0.924% loss in porosity, the maximum stress of lightweight SAB designed insoles is extremely decreased by 19.2917%.

The biomechanical design of the lightweight full contact insole based on SAB can effectively redistribute stress, avoid stress concentration and improve the mechanical properties of the ergonomic individual insole.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

Recent research advocates that there are plenty of opportunities for key players in the offsite construction (OSC) sector to harness the full potential of advanced project management techniques. While previous research mainly focuses on transformations related to digital and advanced technologies driven by industry 4.0 principles, a research gap still exists on the intersection of project management capabilities and OSC. This study attempts to bridge this gap by capturing the homogeneity of different capabilities and integrating them into an overarching framework.

A scientometric analysis is conducted to provide an overview of the co-occurrence network of keywords in the representative studies. A systematic literature review (SLR) of articles published between 2010 and 2022, followed by a subsequent full-text examination of 63 selected articles, revealed 34 interrelated capabilities to be categorised under three exhaustive planning-oriented, design-oriented and delivery-oriented groups.

This review revealed an upward trend of publication on project management capabilities for OSC with a specific interest in optimisation of resources allocated to offsite operations. The top five capabilities discussed more frequently in the literature include (1) artificial intelligence for design error detection, (2) enhanced resource productivity, (3) cost saving in offsite production, (4) real-time traceability of modules and (5) applying lean agile production principles to OSC, which imply the critical role of quality, cost saving, traceability and agility in OSC.

This study elicits core capabilities and develops a new offsite project management framework for the first time. The authors provide directions for researchers and practitioners to apply capabilities for obtaining better outcomes and higher value out of offsite operations.

Arthroscopic osteochondroplasty is a minimally invasive procedure that has been used to treat femoroacetabular impingement syndrome, leading to significant improvements in patients’ clinical outcomes and quality of life. However, some studies suggest that inadequate bone resection can substantially alter hip biomechanics. These modifications may generate different contact profiles and higher contact forces, increasing the risk of developing premature joint degeneration. To improve control over bone resection and biomechanical outcomes during arthroscopic osteochondroplasty surgery, this study aims to present a novel system for measuring femoroacetabular contact forces.

Following a structured design process for the development of medical devices, the steps required for its production using additive manufacturing with material extrusion and easily accessible sensors are described. The system comprises two main devices, one for measuring femoroacetabular contact forces and the other for quantifying the force applied by the assistant surgeon during lower limb manipulation. The hip device was designed for use within an arthroscopic environment, eliminating the need for additional portals.

To evaluate its performance, the system was first tested in a laboratory setup and later under in-service conditions. The 3D printing parameters were tuned to ensure the watertighness of the device and sustain the intraoperative fluid pressures. The final prototype allowed for the controlled measurement of the hip contact forces in real-time.

Using additive manufacturing and readily available sensors, the present work presents the first device to quantify joint contact forces during arthroscopic surgeries, serving as an additional tool to support the surgeon’s decision-making process regarding bone resection.

There are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.

First, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.

Through simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.

Both the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.