University of Engineering and Technology Peshawar

This research presents a novel procedure of using stationary waves generated with the impulse-response test using semi-noncontact measurements and relatively high-frequency bandwidths. The time histories of vibrations are recorded with a condenser microphone over a regular square grid at the surface of a concrete plate. The analyses were performed on the normalized time-domain response at each test location. Using the impact-normalized time-domain responses, oscillatory modes are obtained through empirical mode decomposition. The first oscillatory mode of each normalized response is converted back to the frequency domain, and its maximum amplitude is shown to be an accurate defect indicator. The defects are effectively delineated spatially, and their intensity is estimated by using extreme value outlier analysis. The physical basis for the proposed procedure is demonstrated by using a finite-element model that indicates that stationary acoustic waves are generated by an impact above the concrete surface. An experimental specimen with simulated defects in the form of shallow and deep delamination, debonding, and honeycomb was used to validate the proposed procedure. Using the proposed experimental and the new data analysis procedure, the defect indicator was able to detect the delamination at two different depths, debonding, and honeycomb in the lab-scale concrete plate. The level of accuracy of the damage detection was comparable to that of more refined yet time-consuming ultrasonic shear-wave echo method. The defect indicator calculated with the proposed signal processing algorithm was found to be robust to the ambient noise that can influence the measurements with the condenser microphone.

Concrete demolition is increasingly important due to global infrastructure renewal, urban densification, and the need for sustainable end-of-life management of built assets. Yet in many developing countries, including Pakistan, demolition practices remain labor-intensive, environmentally damaging, and technologically outdated. This review synthesizes global knowledge on mechanical, explosive, hydraulic, chemical, thermal, and robotic demolition techniques, evaluating them through sustainability, safety, operational efficiency, and circular-economy lenses. A systematic review of international literature, technical standards, and policy reports enabled the development of a structured classification framework and performance indicators encompassing cost, time, safety, environmental impacts, and material recoverability. Results show that although mechanical demolition remains the most widely used method, advanced and low-impact approaches such as hydrodemolition, diamond-wire cutting, selective dismantling, and robotic demolitionachieve lower dust and vibration levels, higher worker safety, and improved resource recovery. Emerging innovations, including BIM-enabled planning, AI-based decision support, remote-controlled robotics, and automated waste segregation, offer substantial potential for Pakistan. The review concludes that achieving sustainable demolition in Pakistan requires regulatory reform, workforce capacity building, and strategic investment in modern technologies to reduce environmental impacts and enhance circular material flows.

Accurate prediction of fracture energy (Gf) in asphalt mixtures is important for durable asphalt pavements designing. Traditional experimental approaches are reliable but need resources, whereas numerical simulations, such as finite element models (FEM), offer flexibility but needs accurate input parameters and calibration. Recent advances in machine learning offer rapid prediction capabilities; however, interpretability and physical relevance remain challenging in this regard. This study presents a hybrid framework that integrates experimental Single Edge Notch Beam (SENB) tests, finite element simulations, and machine learning models to predict fracture parameters for asphalt mixtures. Experimental testing quantified fracture energy, while FEM simulations replicated the fracture response numerically. Machine learning models, including Linear Regression, Gradient Boosting, and AdaBoost, were trained on mixture properties such as stability, flow, air voids, and Stiffness Modulus at 20 °C (ITSM20) to predict surrogate fracture energy. A novel, dimensionally consistent surrogate equation was proposed to link key mixture properties to fracture energy, validated against both experimental and numerical results. The surrogate model demonstrated best accuracy with a mean relative error compared to experimental data. This novel integrated approach, adopted in this study, provides a practical and physics-guided methodology for rapid and reliable prediction of fracture behavior in asphalt mixtures, bridging experimental observations, numerical simulations, and data-driven machine learning modeling, and offering insights for mixture optimization and pavement design.