Infrastructure Engineering - Research Publications
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ItemNo Preview AvailableOptimized Bridge Maintenance Strategies: A System Reliability-Based Approach to Enhancing Road Network Performance(ASCE-AMER SOC CIVIL ENGINEERS, 2024-03-01)
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ItemNo Preview AvailableThe impact of osteoporosis and diabetes on fracture healing under different loading conditions(ELSEVIER IRELAND LTD, 2024-02)BACKGROUND: Osteoporosis and diabetes are two prevalent conditions among the elderly population. Each of these conditions can profoundly influence the fracture healing process by disturbing the associated inflammatory process. However, the combined effects of osteoporosis and diabetes on fracture healing remain unclear. Therefore, the purpose of the present study is to investigate the role of osteoporosis and diabetes in fracture healing and the underlying mechanisms by developing numerical models. METHOD: This study introduces a numerical model that consists of a three-dimensional model of a tibia fracture stabilized by a Locking Compression Plate (LCP), coupled with a two-dimensional axisymmetric model which illustrates the transport and reactions of cells and cytokines throughout the inflammatory phase in early fracture healing. First, the model parameters were calibrated using available experimental data. The model was then implemented to predict the healing outcomes of fractures under five varied conditions, consisting of both osteoporotic and non-osteoporotic bones, each subjected to different physiological loads. RESULTS: The instability of the fracture callus can significantly escalate in osteoporotic fractures (e.g., when a 150 N physiological load is applied, the unstable region of the osteoporotic fracture callus can reach 26 %, in contrast to 12 % in non-osteoporotic fractures). Additionally, the mesenchymal stem cells (MSCs) proliferation and differentiation can be disrupted in osteoporotic fracture compared to non-osteoporotic fractures (e.g., on the 10th day post-fracture, the decrease in the concentration of MSCs, osteoblasts, and chondrocytes in osteoporotic fractures is nearly double that in non-osteoporotic fractures under a 150 N). Finally, the healing process of fractures can suffer significant impairment when osteoporosis coexists with diabetes (e.g., the concentration of MSCs can be drastically reduced by nearly 37 % in osteoporotic fractures under diabetic conditions when subjected to a load of 200 N) CONCLUSIONS: Fracture calluses destabilized by osteoporosis can negatively affect the fracture healing process by disrupting the proliferation and differentiation of mesenchymal stem cells (MSCs). Moreover, when osteoporosis coexists with diabetes, the fracture healing process can severely impair the fracture healing outcomes.
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ItemNo Preview AvailableDevelopment of numerical model-based machine learning algorithms for different healing stages of distal radius fracture healing(ELSEVIER IRELAND LTD, 2023-05)BACKGROUND AND OBJECTIVES: Early therapeutic exercises are vital for the healing of distal radius fractures (DRFs) treated with the volar locking plate. However, current development of rehabilitation plans using computational simulation is normally time-consuming and requires high computational power. Thus, there is a clear need for developing machine learning (ML) based algorithms that are easy for end-users to implement in daily clinical practice. The purpose of the present study is to develop optimal ML algorithms for designing effective DRF physiotherapy programs at different stages of healing. METHOD: First, a three-dimensional computational model for the healing of DRF was developed by integrating mechano-regulated cell differentiation, tissue formation and angiogenesis. The model is capable of predicting time-dependant healing outcomes based on different physiologically relevant loading conditions, fracture geometries, gap sizes, and healing time. After being validated using available clinical data, the developed computational model was implemented to generate a total of 3600 clinical data for training the ML models. Finally, the optimal ML algorithm for each healing stage was identified. RESULTS: The selection of the optimal ML algorithm depends on the healing stage. The results from this study show that cubic support vector machine (SVM) has the best performance in predicting the healing outcomes at the early stage of healing, while trilayered ANN outperforms other ML algorithms in the late stage of healing. The outcomes from the developed optimal ML algorithms indicate that Smith fractures with medium gap sizes could enhance the healing of DRF by inducing larger cartilaginous callus, while Colles fractures with large gap sizes may lead to delayed healing by bringing excessive fibrous tissues. CONCLUSIONS: ML represents a promising approach for developing efficient and effective patient-specific rehabilitation strategies. However, ML algorithms at different healing stages need to be carefully chosen before being implemented in clinical applications.
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ItemChanges in joint lubrication with the degree of meniscectomy and osteochondral junction integrity(Elsevier BV, 2023-11)This study focuses on the relationship between meniscectomy and osteochondral junction health, and their integrity on cartilage lubrication. Using a previously published multi-component joint computational model, we explored the impact of increasing degree of meniscectomy and osteochondral flow conductivity on joint lubrication. Results suggest a greater effect of meniscectomy on joint lubrication when the osteochondral junction is healthy. However, the impact is less pronounced when the osteochondral junction is already diseased due to compromised lubrication capability. This research provides a first-time quantitative analysis of this interaction, which highlights the importance of adequately evaluating the osteochondral junction’s condition before meniscectomy surgery. It also suggests that reducing post-surgery activity level may be beneficial for patients with diseased junctions undergoing meniscectomy.
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ItemNo Preview AvailableInfluences of variability and uncertainty in vertical and horizontal surface roughness on articular cartilage lubrication(PERGAMON-ELSEVIER SCIENCE LTD, 2022-09)BACKGROUND AND OBJECTIVES: Cartilage surface roughness has significant implications on joint lubrication. However, the effects of the variability in surface roughness in different directions (especially in horizontal direction) in mixed-mode lubrication have not been fully investigated and relevant research work in this field is limited. This study presents a probabilistic numerical approach to investigate the influence of variability and uncertainty of Root-Mean-Square (RMS) roughness heights (vertical roughness) and roughness correlation lengths (horizontal roughness) on cartilage lubrication. METHODS: The synthetic surface topographies with typical ranges of vertical and horizontal roughness characteristics were firstly input to a coupled cartilage contact model. A response surface was then constructed using the input roughness parameters and the output coefficient of friction (CoF). Finally, a large number of independent or correlated roughness samples were generated for computing the probability of mixed-mode lubrication failure (PoF), which was defined as CoF > 0.27 (corresponding to a 90% loss of fluid support in the contact interface). RESULTS: Both independent RMS roughness heights and correlation lengths are correlated positively with CoF. This indicates that the increase of the vertical surface roughness could exacerbate cartilage wear, whereas increasing surface roughness in horizontal direction (i.e., reducing correlation lengths) could retain gap fluid that aids mixed-mode lubrication. Importantly, it shows that CoF is dominant by RMS roughness height. The uncertainty in the independent correlation lengths may lead to the underestimation of PoF. By simulating osteoarthritic surface roughness with a strong correlation between RMS roughness heights and correlation lengths, the value of PoF could reach 70-99%. CONCLUSION: This study highlights the significance of incorporating the mutual relations between the surface roughness in vertical and horizontal directions into research, and the findings could potentially contribute to the design of biomimetic cartilage surfaces for the treatment of osteoarthritis.
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ItemOsteochondral junction leakage and cartilage joint lubrication(ELSEVIER IRELAND LTD, 2023-03)BACKGROUND AND OBJECTIVES: Previous studies have shown that there is potentially interstitial fluid exchange between cartilage tissue and the subarticular spongiosa region in the case of injury or disease (e.g., osteoarthritis and osteoporosis). Interstitial flow is also required for cartilage lubrication under joint load. A key question then is how cartilage lubrication is modified by increased interstitial fluid leakage across the osteochondral junction. Thus, the purpose of this study is to develop a numerical model to investigate changes in cartilage lubrication with changes in osteochondral junction leakage. METHODS: The multi-phase coupled model includes domains corresponding to the contact gap, cartilage tissue and subchondral bone plate region (ScBP). Each of these domains are treated as poroelastic systems, with their coupling implemented through mass and pressure continuity. The effects of osteochondral junction leakage on lubrication were investigated with a parametric study on the relative permeability between the ScBP and cartilage tissue. RESULTS: Significant effects of ScBP permeability were predicted, especially during the early stage of the junction leakage development (early stage of the disease). There is a significant reduction in mixed-mode lubrication duration under the effect of increased junction leakage (the cartilage tissue mixed-mode lubrication duration is about 33% decrease for a relative permeability ratio of 0.1 between ScBP and cartilage tissue, and about 52% decrease under the osteoarthritis condition). In addition, the time for cartilage to reach steady-state consolidation is significantly reduced when ScBP permeability increases (the consolidation time reduces from roughly 2 h to 1.2 h when the relative permeability ratio increases from 0.001 to 0.1, and it reduces to 0.8 h for an advanced osteoarthritis condition). It is predicted that the initial friction coefficient could increase by over 60% when the ScBP permeability is consistent with an advanced osteoarthritis (OA) condition. CONCLUSION: Increased osteochondral junction leakage induced by joint injury and disease could result in increased cartilage surface wear rates due to more rapid interstitial fluid depressurization within articular cartilage.
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ItemNo Preview AvailableComputational Modelling for Managing Pathways to Cartilage Failure.(Springer Cham, 2023)Over several decades the perception and therefore description of articular cartilage changed substantially. It has transitioned from being described as a relatively inert tissue with limited repair capacity, to a tissue undergoing continuous maintenance and even adaption, through a range of complex regulatory processes. Even from the narrower lens of biomechanics, the engagement with articular cartilage has changed from it being an interesting, slippery material found in the hostile mechanical environment between opposing long bones, to an intriguing example of mechanobiology in action. The progress revealing this complexity, where physics, chemistry, material science and biology are merging, has been described with increasingly sophisticated computational models. Here we describe how these computational models of cartilage as an integrated system can be combined with the approach of structural reliability analysis. That is, causal, deterministic models placed in the framework of the probabilistic approach of structural reliability analysis could be used to understand, predict, and mitigate the risk of cartilage failure or pathology. At the heart of this approach is seeing cartilage overuse and disease processes as a 'material failure', resulting in failure to perform its function, which is largely mechanical. One can then describe pathways to failure, for example, how homeostatic repair processes can be overwhelmed leading to a compromised tissue. To illustrate this 'pathways to failure' approach, we use the interplay between cartilage consolidation and lubrication to analyse the increase in expected wear rates associated with cartilage defects or meniscectomy.
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ItemStructural Health Monitoring of Bridges Using Advanced Non-destructive Testing Technique(Springer, Singapore, 2020-01-01)This paper presents an integrated framework for structural health monitoring of bridges by using advanced non-destructive testing (NDT) technique in conjunction with computational modelling. First, the structural characteristics of the Eltham Trestle Bridge under train loading were monitored using the combination of the 3D optical measurement system and IBIS-S. The results demonstrate that, in conjunction with computational modelling, the NDT can capture the structural health conditions of the bridge by analysing the natural frequencies and deformation profiles of the critical members of the bridges. Then, the developed framework also takes into account the impact of extreme events (e.g. truck impacts and earthquakes) by using a reliability-based model. Finally, using the Montague Street Bridge as a case study, it shows that proposed framework has the capability of predicting the residual life of a bridge subject to both progressive deterioration and extreme events throughout its service life.
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ItemAutomation in structural health monitoring of transport infrastructure(Springer Singapore, 2021-01-01)Roads are among the most important assets in the world. Road structure improvements make a crucial contribution to economic development and growth and bring important social benefits. Automation in structural health monitoring allow the accurate prediction of ongoing damage caused by long-term traffic loading. This permits optimal road structure management and ensures the longevity and safety of road structures. This chapter discusses a variety of advanced automation techniques in structural health monitoring of road structures, such as data acquisition, data processing, and life-cycle assessment. It demonstrates that the implementation of automation in road asset management can increase the productivity and extend the service life of road structures, and enhance the durability of crucial road structures and increase transport infrastructure sustainability.
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ItemDetecting structural damage to bridge girders using radar interferometry and computational modelling(JOHN WILEY & SONS LTD, 2017-10)The process for assessing the condition of a bridge involves continuously monitoring changes to the material properties, support conditions, and system connectivity throughout its life cycle. It is known that the structural integrity of bridges can be monitored by measuring their vibration responses. However, the relationship between frequency changes and structural damage is still not fully understood. This study presents a bridge condition assessment framework which integrates computational modelling and noncontact radar sensor techniques (i.e., IBIS-S) to predict changes in the natural frequencies of a bridge girder as a result of a range of parameters that govern its structural performance (e.g., elastomeric bearing stiffness, concrete compressive stiffness, and crack propagation). Using a prestressed concrete bridge in Australia as a case study, the research outcomes suggest that vibration monitoring using IBIS-S is an efficient way for detecting the degradation of elastomeric bearing stiffness and shear crack propagation in the support areas that can significantly affect the overall structural integrity of a bridge structure. However, frequency measurements have limited capability for detecting the decrease in the material properties of a bridge girder.