Journal of Recent Activities in Infrastructure Science

Investigating Carbon Emissions in the Healthcare Sector in Pakistan Based on Life Cycle Assessment

Muhammad Labib — 2026-07-01

Healthcare buildings are energy-intensive facilities, yet their lifecycle carbon emissions remain insufficiently studied in Pakistan. This study applies a Building Information Modelling (BIM)-integrated, cradle-to-grave Life Cycle Assessment to a single-storey healthcare facility in Murree, Pakistan, over a 50-year service life. Autodesk Revit 2024 was used to model the building and evaluate eight wall materials, five plaster materials, six insulation materials, and five roof systems. Thermal resistance, heat-transfer coefficients, peak heating load, and energy demand were calculated, while operational emissions were estimated using Pakistan’s grid emission factor of 0.52 kg CO₂/kWh. Embodied carbon was quantified from BIM-derived material quantities and published emission factors. Lightweight concrete blocks (W1) produced the largest reduction in peak heating load and associated CO₂ emissions (56.5%) relative to conventional clay brick walls. Lightweight plaster (P1), 2-inch polyurethane board insulation (INS1), and the high-performance roof system (R1) achieved reductions of 41.68%, 18.09%, and 19.51%, respectively. The optimized envelope reduced embodied carbon from 106.84 to 79.98 t CO₂ (25.1%) and lowered modelled total lifecycle CO₂ emissions by 41.92%. These findings demonstrate that practical envelope-material substitutions can substantially reduce carbon emissions from Pakistani healthcare buildings and provide quantitative guidance for hospital design, retrofit planning, and low-carbon building policy.

Nano-Engineered Geopolymer Concrete Incorporating Graphene, Nano-Silica, and Self-Healing Agents for Ultra-Durable Structures

Koyye Rahul — 2026-07-23

In this study, an experimental study was conducted to examine the mechanical properties, durability, and non-destructive assessment of nano-engineered M25-grade geopolymer concrete incorporating graphene nanoplatelets, nano-silica, and self-healing agents to develop ultra-durable and sustainable materials. For this purpose, 20 geopolymer concrete mixes were produced with a fixed fly ash/GGBS binder ratio (60:40), with varying nano-silica (0–4%), graphene (0–0.10%), and self-healing agents (0–2%). The experimental testing included compressive strength, flexural strength, rebound hammer test, ultrasonic pulse velocity (UPV), water absorption, sorptivity, acid resistance, sulphate resistance, and rapid chloride penetration test. The optimum mix (M16), having 2.0% nano-silica, 0.07% graphene, and 1.5% self-healing agent, showed the highest compressive strength of 42.6 MPa and flexural strength of 5.34 MPa, which were increased by 49.47% and 54.34%, respectively, compared to the control mix. Mix M16 had the maximum rebound number, i.e., 45 and ultrasonic pulse velocity of 4.36 km/s, signifying the good quality of concrete along with remarkable improvement in durability, such as water absorption 42.2% less, sorptivity 48.7% less, acid-induced strength loss 59.5% less, sulphate-induced strength loss 59.1% less, and chloride ion permeability decreased by 52.4%.

Repairing Concrete Cracks Using Bio-Influenced Self-Healing Techniques

Shaik Pedda Baji — 2026-07-09

This study aims to evaluate the crack-healing efficiency of bioconcrete by incorporating microorganisms and suitable nutrient precursors into the concrete matrix, to improve structural durability, reduce permeability, and extend the service life of concrete. When cracks form and water enters, dormant microorganisms activate, producing minerals that seal the fissures, enhancing concrete strength and longevity. An experimental program tested self-healing under various conditions, using methods like integrating bacteria into the mix, immobilizing them in lightweight aggregates, and combining with graphite nanoplatelets. Calcium lactate replaced about 5% of cement as a precursor. Specimens were cracked at intervals (3, 7, 14, and 28 days) to assess healing effectiveness. Results showed that bacteria in graphite nanoplatelets were more effective after 3 and 7 days, while lightweight aggregates excelled at 14 and 28 days, significantly improving compressive strength in the latter configuration. Furthermore, the bacterial self-healing mechanism effectively reduced crack width and water permeability, thereby enhancing the durability and service life of the concrete. The study concluded that the use of bacteria with suitable carrier materials offers a sustainable and eco-friendly alternative to conventional concrete repair techniques while minimizing long-term maintenance costs.

Digital Twins and Artificial Intelligence for Smart Infrastructure: Applications, Challenges, and Future Directions

Mahadeva M. — 2026-07-30

The swift evolution of technologies like artificial intelligence (AI), the Internet of Things (IoT), Building Information Modeling (BIM), cloud computing, and sensor technology has dramatically reshaped how society manages modern civil infrastructure. Among these innovations, the Digital Twin (DT) has emerged as a standout technology: a dynamic virtual representation of physical infrastructure that continuously exchanges real-time data with its physical counterpart. By merging AI with DT, infrastructure systems can engage in smart monitoring, predictive maintenance, fault diagnosis, performance optimization, and even autonomous decision-making throughout their lifecycle. This powerful combination boosts operational efficiency, cuts down maintenance costs, enhances safety, and promotes sustainable infrastructure development. This study reviews the integration of DT technology with AI for smart infrastructure applications. It explores the core concepts, enabling technologies, architecture, and lifecycle of AI-driven DT, while showcasing their applications across buildings, bridges, transportation networks, highways, railways, water distribution systems, and smart cities. Additionally, it discusses the latest advancements in machine learning, deep learning, computer vision, generative AI, large language models (LLMs), and physics-informed AI that elevate the capabilities of DT for infrastructure monitoring and management. It also critically analyzes existing literature to pinpoint current achievements, implementation strategies, and technological progress across various civil engineering fields. The review highlights several key research challenges, such as data interoperability, cybersecurity, computational complexity, scalability, standardization, data quality, and model validation. It also explores exciting future research opportunities in areas like explainable AI, autonomous DT, edge computing, 6G communication, quantum computing, and sustainable infrastructure management.

A Regression-Based Predictive Model for the Compressive Strength of All-in-Laterite Concrete

Eghosasere Oluwaseyi Rowland-Lato — 2026-07-21

All-in-laterite concrete, in which lateritic material supplies both the fine and coarse aggregate fractions, is an attractive, low-cost, locally sourced alternative to conventional concrete in tropical regions, yet its adoption is constrained by the lack of a validated tool to predict compressive strength from mix-design variables. This study characterises all-in-laterite concrete produced from laterite from Idah (Kogi State, Nigeria) and develops a regression-based predictive model of its compressive strength. Laterite fine and laterite rock aggregates were tested for specific gravity, water absorption, particle-size distribution, abrasion resistance, and oxide composition. A total of 108 cubes were cast across three mix proportions (1:2:4, 1:1½:3, 1:1:2) and water–cement ratios from 0.65 to 1.25, and tested for compressive strength at 7, 14, 28, and 64 days, alongside slump and hardened density. The aggregates were normal-weight (bulk specific gravity 2.32–2.48) but highly absorptive (7.95–13.5%), and the coarse fraction was mechanically sound (Los Angeles abrasion 25.56%). Strength followed the classical inverse relationship with the water–cement ratio, with peak 28-day strengths of 18.8–23.4 MPa rising to 21.0–26.9 MPa at 64 days. Twelve mixes (48 strength observations) were pooled and fitted by ordinary least squares to a model of the form fc = β₀ + β₁(w/c) + β₂(a/c) + β₃ln(t), which achieved R² = 0.936 (adjusted R² = 0.932, RMSE = 1.26 MPa); a simplified 28-day model achieved R² = 0.942. Leave-one-mix-out cross-validation returned Q² ≈ 0.91 for both models. All-in-laterite concrete from the studied source is a viable normal-weight structural material for lightly-loaded and non-structural applications when proportioned on an effective-water basis, and its 28-day strength can be predicted to within about ±1.3 MPa from three routinely available mix-design variables.