In this study, shear-critical reinforced concrete (RC) beams were strengthened by combining the prestressing and near-surface mounted (NSM) rods approaches. The potential danger of failure in such RC beams is a substantial concern as it is considered a potential threat. This study addresses its careful mitigation through experimental identification and numerical analysis to enhance the safety and sustainability of buildings by reducing the probability of failure risk for these RC beams. Nine of the ten RC beams that were tested had strengthened, and one had not. Internal prestressing (IP) within the beam body, external prestressing NSM (PNSM), internal embedment (IE) inside the beam with or without prestressing, and NSM are the strengthening technologies that were employed. The range of the extra shear reinforcement ratios (μs) was 0.87% to 1.60%. We investigated how strengthened beams behaved structurally in terms of the cracking load, ultimate load, load–deflection response, ultimate deflection, and stiffness. The insertion of five pairs of PNSM rods (μs = 1.45%) and five pairs of IP rods (μs = 1.6%), respectively, increased the beams’ shear capacity by 57.8% and 70.4%. Shear capacity increased by 23.2% when three pairs of IE rods (μs = 1.02%) were installed. The prestressing location had an impact on shear capacity, with the interior case surpassing the external one. Compared to the control, the stiffness of the strengthened beams rose by 20%, 82%, and 84.4% when three, four, or five pairs of internal prestressing rods were added. A formula is proposed to calculate the shear capacity of all beams strengthened using various methods.

Value Management (VM) of construction projects is beset by inherent pitfalls of expertise-dependence, fixed processes, and segregation from data-rich environments. The following paper presents and evaluates an artificial intelligence-facilitated Value Management System (AIVMS) that incorporates predictive analytics, Multi-Criteria Decision-Making (MCDM), and Explainable AI (XAI) to facilitate open, fact-based stakeholder-centric decisions throughout project life cycles. It was designed using the Design Science Research approach on systematic literature review of 127 peer-reviewed papers and was validated with three-round Delphi study with 24 construction professionals. The AIVMS system is six-layered and consists of: intelligent value driver identification, predictive analytics engine, dynamic MCDM engine, integration and optimization core, explainable AI interface, and adaptive learning system. Empirical validation through three real-world project case studies revealed significant improvements: 23% increase in decision-making consistency, 31% reduction in value engineering cycle time, and 89% improvement in stakeholder satisfaction with transparency of decisions. The framework achieved 91.2% precision for forecasting a variety of performance measures and enabled the identification of €2.8 M average cost optimization potential. This research is the first empirically-validated integration of AI, MCDM, and XAI for construction value management that integrates machine-based intelligence with man-centric transparency requirements and provides real-world implementation avenues for existing BIM and project management systems.

The heavy environmental impact of the construction industry—responsible for 39% of world CO₂ emissions and consuming over 40% of natural resources—supports the need for evidence-based decision-making tools for sustainable material selection balancing environmental, economic, and social considerations. This research develops and evaluates an integrated decision support system that couples cradle-to-grave lifecycle assessment (LCA) with various multi-criteria decision-making (MCDM) methods to optimize climate-resilient material selection for schools. The methodology is an integration of hybrid Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) and VIKOR techniques validated with eight case studies in hot-arid, hot-humid, and temperate climates. Environmental, economic, social, and technical performance indices were evaluated from primary experimental data and with the input from 22 international experts with climate change assessment expertise. Ten material options were examined, from traditional, recycled, and bio-based to advanced composite systems throughout full building lifecycles. The results indicate geopolymer–biofiber composite systems achieve 42% reduced lifecycle carbon emissions, 28% lower cost of ownership, and 35% improved overall sustainability performance compared to traditional equivalents. Three MCDM techniques’ cross-validation demonstrated a satisfactory ranking correlation (Kendall’s τ = 0.87), while Monte Carlo uncertainty analysis ensured framework stability across 95% confidence ranges. Climate-adaptive weighting detected dramatic regional optimization contrasts: thermal performance maximization in tropical climates and embodied impact emphasis in temperate climates. Three case studies on educational building projects demonstrated 95.8% accuracy in validation of environmental performance and economic payback periods between 4.2 and 6.8 years in real-world practice.

In many construction applications, including bridge pedestals, concrete corbels, and concrete anchors, the concrete’s local compressive strength attribute (bearing) is crucial. One of the benefits from concrete’s bearing is its role in mitigation construction failure risk and increase the safety of the buildings. The local compression characteristics of fully hardened concrete were the primary focus of earlier study, with less attention paid to early age concrete (less than 28 days). In order to evaluate the bearing qualities of early age concrete—here defined as the first month—the current experimental program is being carried out. While the bearing plate’s area (Ab), which was placed in the middle of each block’s top surface, differed in dimension (100 × 100 mm, 80 × 80 mm, 60 × 60 mm, and 40 × 40 mm), the concrete pedestals’ size remained constant at 250 × 250 × 200 mm. Tests were conducted on sixteen concrete supports. Four equal groups of samples were created, and each group underwent testing at a different age (T = 3, 7, 15, and 28 days). In each group, unloaded-to-loaded area is varied (A₁/A_b = 6.25, 9.76, 17.36, and 39). The failure, bearing stress–slip curve, ultimate bearing strength and ultimate associated deformation of the tested concrete supports were studied. The results showed that the compressive and tension strengths increased by 178% and 244% when the concrete age reached 28 days compared to 3 days-concrete. As A₁/A_b or/and concrete age increased, the bearing characteristics improved more. The ultimate bearing strength increased by 51%, 56.5%, and 69.5% at $\sqrt{{\displaystyle \frac{A_1}{A_b}}}$ = 6.25 when the samples’ concrete age increased from 3 to 7, 15, and 28 days. The main contribution of this study is a novel formula to forecast the concrete’s bearing strength while accounting for the impact of the concrete’s age and the ratio $\sqrt{{\displaystyle \frac{A_1}{A_b}}}$.

Strengthening lapped spliced reinforced concrete (RC) beams using tiny-diameter steel wires as near-surface-mounted (NSM) rods has not been carried out previously. Thus, the purpose of this work is to examine the behavior of RC beams with insufficient lap splices that are strengthened by NSM steel wires with different schemes to improve durability, efficiency, and effectiveness. At the middle of the beam, a splice length equal to 25 times the diameter of the rebar was used to join two tension bars. Many different schemes were implemented in strengthening the splice region, such as attaching longitudinal wires to the sides and/or bottom of the beam in different quantities with/without end anchorage, placing perpendicular and inclined U-shaped wires at the splice region in different quantities, and implementing a network of intersecting and opposite wires in two different directions. The effect of variables on the behavior of strengthened beams was studied. The findings proved that when the longitudinal wire reinforcement-to-lapped rebars area ratio was 9.4%, 18.7%, and 28%, the ultimate load of the beams was improved by 15.71%, 71.43%, and 104.57%, respectively. When the transverse U-shaped wire reinforcement ratio was 0.036, 0.051, 0.064, 0.075, and 0.150, the ultimate load of the beams was improved by 3.7%, 20%, 31.4%, 50%, and 80%, respectively, and the ultimate deflection was enhanced by 2%, 32%, 19%, 67%, and 62.4% compared to the unstrengthened beam.

While the development of sustainable construction materials, such as green concrete made from glass waste or recycled concrete aggregate, has been extensively researched, much of the existing work has focused narrowly on these two components. This limited scope highlights the need for further investigation to comprehensively address their drawbacks and expand the available knowledge base. Moreover, the current study uniquely emphasizes the shear response of green concrete, a critical aspect that has not been previously explored. Push-off shear samples made of green concrete, a mixture of recycled concrete, and glass waste, were built and subjected to direct shear loading testing to investigate shear response. In different proportions (0, 10, 25, 50, and 100%), fine glass aggregate is used in place of river sand. At different ratios (0, 10, 20, and 40%), coarse glass aggregate was substituted for coarse natural aggregate to form four mixtures. Additionally, recycled concrete and coarse glass aggregates were utilized instead of coarse natural aggregates. In the last group, coarse natural aggregate was substituted with recycled concrete aggregates in different proportions (0, 16, 40, and 80%). Measurements were made of the applied shear force and the sliding of the shear transfer plane during the test. The tested mixtures’ failure, shear strength, shear slip, shear stiffness, and shear stress slip correlations were examined. According to the results, all of the samples failed in the shear transfer plane. The shear strength of mixes containing 10, 25, 50, and 100% fine glass was, respectively, 12.8%, 14.7%, 29.5%, and 39% lower than the control combination without fine glass. As the amount of recycled glass and concrete materials grew, so did the shear slip at the shear transfer plane. In recent years, numerous studies have proposed formulas to predict the push-off shear strength of plain concrete, primarily using compressive strength as the key parameter—often without accounting for the influence of infill materials. The present study introduces an improved predictive model that incorporates the contents of recycled concrete aggregate, coarse glass aggregate, or fine glass aggregate as correction factors to enhance accuracy.