Journal of Building Material Science

Experimental Investigation on the Structural and Economic Feasibility of Light-Transmitting Concrete Using Industrial By-Products

2026-05-28T16:53:07+08:00

The cement manufacturing process emits significant greenhouse gases and consumes substantial energy. The current situation demands alternative sustainable materials to cement in concrete production. On the other hand, rapid urbanization has led to the vertical expansion of modern structures, worsening daylight loss on lower floors, where artificial lighting is now essential. Innovative construction materials that enhance natural illumination are needed due to the energy crisis. A promising approach is light-transmitting concrete, which uses resin matrices, optical fiber, or polymer rods as light-guiding media. In the present study, plastic optical fiber was used as a light-transmitting material in the concrete. This study demonstrates the potential use of industrial by-products, including ground granulated blast furnace slag (0%, 10%, 20%, 30%, 40%) and micro silica (0%, 5%, 10%, 15%, 20%), as effective pozzolanic materials as cement substitutes in concrete to study the strength and durability properties of concrete experimentally. The economic and ecological feasibility of different concrete mix proportions was investigated. The optical fiber (1%) with the optimized percentage of industrial wastes, i.e., slag (20%) and micro silica (10%), was used in the concrete to study different properties of light-transmitting concrete. Results showed that the incorporation of industrial wastes with optical fiber (1%), used in the light-transmitting concrete, improved the performance and produced sustainable end products compared to the conventional concrete.

Design of a Computational Model for 3D Concrete Printed Geometry Using Machine Learning and Genetic Optimization

2026-04-13T14:27:02+08:00

3D Concrete Printing (3DCP) is an emerging technology with well-established benefits and the potential to dramatically change the construction industry. While research in material and process optimization is gaining traction, the architectural application of 3DCP remains relatively underdeveloped. Among many challenges, a lack of suitable computational modelling techniques is often identified as a major obstacle, resulting in simplistic design solutions that do not take full advantage of 3DCP technology. This study proposes a fabrication-aware design model using machine learning (ML), specifically genetic optimization, to address the research gap. 3DCP is used to produce sacrificial formwork for freeform reinforced concrete shell structures. The model conceptualizes a module-based approach to establish interlinked feedback loops across the various stages of a project, enabling fabrication and assembly considerations in the early design phase. Structural behaviour, printability, and segmentation constraints are translated into evaluative criteria within a unified computational workflow implemented in Rhino/Grasshopper, using the Galapagos genetic optimization solver. The framework enables iterative exploration of design options while accounting for both geometric and fabrication-related constraints. Three shell typologies are used to demonstrate the method, including a cantilever, a bridge, and a wall element, supported by a full-scale 3D printed segment for initial validation. This approach enables designers to develop geometries that are specifically tailored to the constraints and opportunities of 3DCP, opening new possibilities for meaningful interaction with the design-to-fabrication pipeline of complex 3DCP geometries.

Assessing Eco-Efficiency of Building Materials Using Type-2 Fuzzy AHP–TOPSIS Framework

2026-01-05T13:09:50+08:00

The construction sector urgently needs methods to identify building materials that are both structurally reliable and environmentally efficient. This paper addresses the scientific issue of eco-efficiency assessment under deep uncertainty in life cycle, cost, and performance data for structural concretes. The research objective is to develop a robust decision-support framework that can rank conventional and low-carbon concretes when expert judgements are imprecise, and environmental indicators vary across contexts. To this end, we propose an interval Type-2 fuzzy AHP–TOPSIS model in which criteria weights and material performances are represented as interval Type-2 triangular fuzzy numbers, with Karnik Mendel centroid type-reduction used to obtain weight intervals and type-reduced decision entries. An eco-efficiency index based on normalized life-cycle assessment indicators (GWP, CED, AP), cost, compressive strength, and service life is used as an external validation target. The framework is demonstrated on a detailed case study comparing OPC, PPC, GGBS, recycled-aggregate, fly-ash, and geopolymer concretes. Results show that geopolymer concrete is consistently the most eco-efficient option and OPC the least, with strong rank concordance between Type-2 TOPSIS closeness coefficients and the eco-efficiency index, and stable top/bottom rankings underweight-band and joint weight-FOU perturbations. Compared with crisp and Type-1 fuzzy AHP-TOPSIS approaches, the proposed model uniquely offers a coherent end-to-end Type-2 pipeline, preserves the footprint of uncertainty in both weighting and ranking, and provides clearer robustness diagnostics for eco-efficiency-oriented material selection.

Mechanical Performance and Sustainability Potential of Concrete Containing Construction and Demolition Waste Recycled Aggregates

2026-03-30T14:18:54+08:00

The construction sector is a major contributor to global carbon emissions and the extinction of resources owing to the heavy use of natural aggregates and production of construction and demolition waste (CDW). The paper examines the viability of using recycled aggregates in the form of CDW as substitutes (0, 10, 30 and 50) of natural coarse aggregates in M25 structural concrete. Experimental testing comprised workability, density, water absorption and compressive strength after 7 and 28 days, which was backed by statistical analysis at 95% confidence level. The findings have shown that the workability reduces with high content of CDW because of augmented water absorption. Internal curing effect enhanced the 10% replacement with the highest compressive strength (31.52 MPa), which was up by 3.5% compared to control. The 30% replacement was found to give similar strength (29.82 MPa) with no statistically significant difference whereas the 50% replacement was found to allow significant reduction as a result of increasing porosity and reducing interfacial transition zones. The replacement level was positively correlated with water absorption, which means that pore connectivity was high. The sustainability analysis indicated possible CO₂ cuts between 6–10% on 30% replacement. The results confirm that at least 30% CDW recycled mixes can be used safely in the structural concrete without affecting the performance, which is in line with the principles of the circular economy and sustainable construction practices.

Value-Chain Governance of Low-Carbon Supplementary Cementitious Materials (SCMs): Trust, Performance, and Blockchain Traceability in the Jordanian Construction Sector

2025-12-16T10:03:09+08:00

The need for a transition to a low-carbon construction industry is met with the need for a nonprofit intermediate system of trust plus institutional mechanisms plus transparency technology. In answering its gaps in knowledge, this study explored whether trust, system of governance, or the use of blockchain technology for transparency impacts performance in the value chain related to SCM-low carbon SCM in Jordan. The methodologies used in answering this issue are mixed methods. Data was obtained from 86 firms across the value chain in SCM. The data was obtained using questionnaires. The reliability and validity of the questionnaires were obtained using Cronbach’s alpha coefficients. Regression analysis was used to establish the relationship between the identified constructs. Agent-based simulation analysis was used to evaluate system-level coordination. Visualization using Gephi was used to establish transparency. The results showed that trust had a strong positive influence on performance, but this was mediated only partially by governance structure. In addition, transparency moderated the relationship between trust and performance positively. Simulation and results analysis showed that transparency increased governance efficiency, which mitigated coordination problems in the SCM system. The results emphasize that behavioural trust and transparency enabled by blockchain technology are complementary mechanisms to leverage within SCM to make these systems more transparent, efficient, and sustainable. The study enhances the generation of socio-technical governance theories related to SCM systems.