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Item Bim enabled design and cost benefit analysis of commercial building before and after clash detection and resolution(UMT. Lahore, 2025) Muhammad Manan Khan; Mazhar Hussain; Umair Ali; Hafiz Muhammad Nabeel; Sadiq ShahDespite being an essential driver of global economic development, the construction industry faces persistent inefficiencies, including cost and time overruns, along with design misalignments. This thesis analyzesthe impact of BIM (Building Information Modeling) technology,specifically its 5D cost estimation and clash detection features. Additionally, a practical case study was carried out on a 4-story commercial plaza where BIM modeling was done on Autodesk Revit and clash detection on Navisworks. This project required the architectural, structural, and MEP (Mechanical, Electrical, and Plumbing) systems to be integrated into a federated 3D model, with construction-relevant clash detection-resolution cycles performed before construction. In this study, three basic conflict types were identified, namely hard, soft, and workflow. Each was simulated and resolved using Navisworks' tools, and a clash resolution cost analysis was performed before and afterresolution.In this case, it was observed that the project cost decreased significantly from Rs 166.81 million to Rs 156.21 million, illustrating a total of Rs 10.6 million saved. Ultimately, these results highlight BIM’s ability to improve interdisciplinary collaboration, reduce project change orders, enhance rework dynamics, improve the overall decision-making process, and drive sustainable constructionItem Automated generation of bim models from architectural and structural plans using artificial intelligence(UMT. Lahore, 2025) Muhammad Asad; Muhammad Soban; Sikander Ali Khan; Muhammad IrfanThis study introduces a new method that uses Artificial Intelligence (AI) and Machine Learning (ML) models to automatically change old two-dimensional architectural and structural plans into Building Information Modeling (BIM) systems. This method helps solve the usual problems and delays found in manual BIM work, offering clear benefits to the Architecture, Engineering, and Construction (AEC) industry. Significantly, the system employs the Mask R-CNN deep learning model within the Detectron2 framework, trained on a dataset comprising 552 annotated elements from real-world projects, to reliably identify and distinguish primary building components including walls, columns, doors, and beams even when analyzing diverse or low-quality plans. The data augmentation technique was used successfully to make the model resistant to various drawing styles and trained on more than 200 iterations and achieved more than 95 percent accuracy in detection. Seen objects are converted to the IFC standard directly, originating 3D BIM models that may immediately be used in BIM programs. The automatized strategy significantly eliminates the amount of manual work, improves the quality of modelling, and assists sustainable management since it allows performing digital analysis of existing buildings. This study also points out to the scalability of the solution to large-scale projects, collaboration of the team due to standardized models, and usefulness in the energy efficiency planning and renovation planning. In conclusion, this research shows that deep learning can effectively automate the BIM process, providing an efficient and scalable way to convert old building records and support digital transformation in construction. Additionally, this work contributes to Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities), highlighting its importance for sustainable growth.Item A comparative study on effect of curing temperature on the properties of silica fume concrete(UMT. Lahore, 2025) Muhammad Haris; Ans Aziz; Zohaib Ahmed; Shayan TariqThis thesis investigates the influence of curing temperature on the mechanical and physical properties of concrete incorporating silica fume as a partial cement replacement. Silica fume, known for its pozzolanic reactivity and ability to enhance concrete strength and durability, was evaluated under various curing conditions including ambient curing, steam curing, and water curing. The experimental findings of this study demonstrate that incorporating silica fume as a partial replacement of cement significantly influences the strength development and workability of concrete. It was observed that higher dosages of silica fume negatively affect workability due to increased water demand, attributed to its high fineness and surface area. However, replacement levels between 5% and 15% yielded the most favorable results, offering an optimal balance between workability and compressive strength. At early ages (3 days), concrete mixes within this replacement range showed improved strength development, indicating the initiation of the pozzolanic reaction. This trend is consistent with previously published literature and confirms that silica fume enhances early-age performance without compromising fresh properties when used in moderate quantities. Moreover, the strength of concrete continued to increase at 28 days, as the pozzolanic reaction became more pronounced over time, contributing to a denser microstructure and higher compressive strength. Additionally, curing at elevated temperatures (such as 37°C) was found to accelerate the rate of hydration and pozzolanic activity, resulting in higher early-age strength compared to curing at ambient or lower temperatures. Overall, the study concludes that 5–15% silica fume replacement, combined with proper temperature-controlled curing conditions, can significantly enhance the mechanical properties of concrete, especially in strength development, while maintaining acceptable workability.Item Design of underground parking(UMT. Lahore, 2025) Muhammad Sher Afghan; Muhammad Shaharyar; Talha Akhtar; Summama ZafaThe increasing number of vehicles on university campuses has created significant challenges related to parking management, spatial utilization, and pedestrian safety. This report covers the structural design of a two-story underground parking facility at the University of Management and Technology (UMT), aimed at addressing current and future parking demands while preserving surface-level green spaces for recreational and academic use. The proposed structure accommodates up to 976 vehicles across two basement levels and is located near the southern gate of the UMT Greens area to optimize accessibility and minimize interference with the existing campus infrastructure. A comprehensive multi-phase approach was undertaken, including site surveys, transportation planning, and structural analysis. The structural design adheres to ASCE and ACI 318-19 standards. Functional layout decisions such as 45 degrees angled parking one-way circulation patterns and modular stall configurations. PCI 129-15 design manual is used referring level of service C, offering a balanced trade-off between comfort and space efficiency.Item A comparative study on strength(UMT. Lahore, 2025) Ali Riaz; Umair Ahmed Qureshi; Muhammad UmerRafique; Shah KhalidConcrete is one of the most commonly used construction materials, and its properties can be modified using additives like air-entraining agents (AEAs) and supplementary cementitious materials. This study focuses on the effects of AE192, an air-entraining agent, on the mechanical and physical characteristics of ceramic concrete blocks, comparing them to traditional first-class bricks. The objective was to evaluate how different percentages of AE192 (5%, 8%, and 10%) influence compressive strength, weight, and workability, while also incorporating fly ash and ceramic powder for sustainability. A 1:2:4 mix design was used, with Bestway OPC Grade 43 cement, Lawrencepur sand, and 9.5 mm coarse aggregate. Fly ash replaced 20% of cement, and ceramic powder replaced 15% of sand. Two sample sets were tested at 7, 14, and 28 days for compressive strength and weight, following ASTM standards for slump, specific gravity, bulk density, and compression testing. Results showed the control mix (0% AE192) had the highest compressive strength (2030.56 PSI in Sample 1 and 1269.44 PSI in Sample 2), making it suitable for structural applications. The 5% AE192 mix showed balanced strength and reduced weight (1841.67 PSI and 733.33 PSI), making it ideal for semi- structural use. Higher AE192 levels (8% and 10%) led to reduced strength but improved workability and freeze-thaw resistance, useful for non-structural applications. Ceramic- enhanced blocks, especially with 10% ceramic powder, achieved 1186.11 PSI, offering a sustainable option. Although first-class bricks had higher strength, they were less sustainable. The study concludes that 5% AE192 offers an optimal balance of strength, workability, and sustainability.Item Xrd analysis of cement paste at elevated casting temperature with and without admixtures(UMT. Lahore, 2025) Umair Jahangir; Abeer Azam; Shoaib ZaffarIn the present research, it was examined how an increased temperature during casting affects the hydration process and phase of cement pastes mixtures in terms of X-ray Diffraction (XRD) measurements. Three cement mixes were examined including C100 (the control mix made with 100 percent cement), C85F15 (the mix with a 15 percent fly ash replacement), and C100AD (the mix made with chemical admixture cement). The curing of each mix was done in three temperatures that included room temperature (the temperature is estimated to be 25◦C), 50 and 70◦C to emulate different environments and accelerated curing situations. Expert High Score Plus software was applied to get XRD analysis in order to define and compare the crystalline phases which appeared as a result of various conditions. The results show that high temperatures remarkably amplify the rate at which reactions involving hydration occur, increase the crystallinity of portlandite (Ca(OH)2) and minimize the occurrence of unreacted silicates (C3S and C2S). During pozzolanic mixes, high temperatures caused activation of fly ash that caused the more portlandite to be consumed and generate more C-A-S-H gel. The improved early hydration was now measured even at room temperature in the admixture modified mix (C100AD) and the best results were recorded at 50◦C. Generally, the thesis concludes that mix and temperature alterations are extremely important in the hydration process, the phase stability, and the microstructure development of cement pastes. These results are significant to enhance strength, durability and efficiency on cementitious materials at an early age that are thermally curedItem Predicting the behaviour of geosynthetic-reinforced soil abutments using machine learning(UMT. Lahore, 2025) Rana Shabahat Qamar; Muhammad Faizan Jamil; Muhammad Arslan; Muhammad Saad; Abdul Salam ChandioGeosynthetic-reinforced soil (GRS) abutments are commonly used in bridge construction, retaining walls, and highway embankments because they are strong, flexible, and cost-effective. One of the most important things to consider when using these structures is how much they might settle, since too much movement can affect safety and how well they work. Traditional models for predicting settlement often make too many simplifying assumptions about how the soil interacts with the reinforcement, while more advanced methods like finite element modeling are accurate but need a lot of time, skill, and computing power. This study uses a machine learning approach called Gene Expression Programming (GEP) to predict how much GRS abutments settle. The researchers used a dataset with 354 experimental observations, where the goal was to predict settlement, and the inputs included factors like surcharge pressure, soil friction angle, reinforcement stiffness, vertical spacing, abutment shape, and the angle of the facing. The GEP model performed very well, with a testing score of 0.91, an RMSE of 5.53, and an MAE of 3.09, which shows it agrees closely with the actual experimental results. Additional checks using separate data confirmed the model's reliability. A sensitivity analysis also showed which factors had the biggest impact on settlement, giving useful guidance for future design improvements. This research shows that machine learning, especially GEP, can be a fast and dependable alternative to older methods for predicting settlement. It supports a more performance-based and data-driven approach in geotechnical engineering.