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Fibre deformation (or shearing of yarns) can develop during the liquid moulding of composites due to injection pressures or polymerisation (cross-linking) reactions (e.g., chemical shrinkage). On that premise, this may also induce potential residual stress–strain, warpage, and design defects in the composite part. In this paper, a developed numerical framework is customised to analyse deformations and the residual stress–strain of fibre (at a micro-scale) and yarns (at a meso-scale) during a liquid composite moulding (LCM) process cycle (fill and cure stages). This is achieved by linking flow simulations (coupled filling–curing simulation) to a transient structural model using ANSYS software. This work develops advanced User-Defined Functions (UDFs) and User-Defined Scalers (UDSs) to enhance the commercial CFD code with extra models for chemorheology, cure kinetics, heat generation, and permeability. Such models will be hooked within the conservation equations in the thermo-chemo-flow model and hence reflected by the structural model. In doing so, the knowledge of permeability, polymerisation, rheology, and mechanical response can be digitally obtained for more coherent and optimised manufacturing processes of advanced composites. [ABSTRACT FROM AUTHOR]

This paper investigates the Building Information Modeling (BIM) adoption impact on the factors of Contract Cost Variance (CCV) over time. The study considers qualitative and quantitative data to identify the most common causes of CCV through pre-tendering. A partial least square-structure model (PLS-SEM) procedure was used to develop a causal model and rank CCV factors based on their effect, partially based on prior survey raw data conducted in 2022 and the data from 94 projects. Construction industry experts assessed the prior five-year rate of BIM adoption on construction projects to infer the expected trend in BIM adoption in the future (until 2037). Based on the causal model of CCV factors and the future rates of BIM adoption, the dynamic impact of BIM on CCV factors over time was modeled and analyzed. The analysis shows that BIM reduces CCV over time by improving Estimator Performance (EP), Information Quality (IQ), and contractual procedure (CP). The results showed that the CP, EP, and EF have directly impacted CCV, and the PC and IQ indirectly affect the CCV. This paper considers the temporal aspect, examining how the impact of BIM on CCV factors evolves. This dynamic analysis is crucial for long-term strategic planning in construction management. [ABSTRACT FROM AUTHOR]

The precast concrete production process faces uncertainties and risks that reduce the efficiency of the Saudi precast concrete industry. Assessing the risk factors' interdependence yields better results than considering individual analyses only. The previous precast risk studies did not consider the interdependencies among risk factors concerning different process stages. This paper aims to identify precast risk factors and prioritize their importance in Saudi Arabia. Using a dynamic Bayesian network, the assessment considers the interrelationships among the risk factors and different production stages. The risk factors were collected from previous studies, evaluated, and classified into the five construction stages by performing structured interviews with ten experts. The probabilities of the root risks and conditional probabilities of intermediate and leaf risks were assessed based on the ten experts. Then, a Monte Carlo simulation was utilized to provide the status of these probabilities, which were considered input data for the GeNIe program. The main results revealed that erection productivity is the highest risk, with a probability value of 0.87. In addition, the design schedule's significance directly impacts production capacity, resulting in a probability value of 0.74. However, the site management risk is low, with a 0.32 probability value. This paper assists practitioners in optimizing construction schedules. [ABSTRACT FROM AUTHOR]