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One of the essential parts of any synthetic aperture radar (SAR) based microwave imaging system for pipeline inspection is the antenna, which determines the system ability to detect defects on an object reconstructed image. As the standoff distance increases, the antenna’s ability to detect defects on a material becomes influenced by the field of operation making it less effective, thereby affecting the defect information used to characterise the reconstructed pipe sample. In this paper, we conduct a comparative analysis of various antennas performances for pipeline inspection in near and far field operations using a SAR-based microwave system. An antipodal Vivaldi antenna (AVA) and 2-AVA are designed, and their performances are compared to an open-ended waveguide (OEW). The defective pipe image is reconstructed using the autofocus range-Doppler algorithm (RDA) for the OEW and AVA and improved multiple signal classification least square estimation (MUSIC-LSE) for the 2-AVA. Simulation and experimental results showed that the three antennas exhibit different performances depending on the region of operations. For the near field inspection, the OEW has a higher signal-to-noise-ratio (SNR) of 19 dB, fewer errors squared error (SE), and COV of 15% compared to the AVA with 16 dB, and 2-AVA with 14 dB, respectively. For the far field inspection, the 2-AVA gives a better SNR of 2 dB compared to the OEW and AVA with SNRs of −5 dB and −3 dB respectively. Thus, the choice of the antenna for the inspection depends on the region of operation.

The construction industry is increasingly focused on sustainability, with a particular emphasis on reducing the environmental impact of cement production. One approach to this problem is to use recycled materials and explore eco-friendly raw materials, such as alumino-silicate by-products like fly ash, which can be used as raw materials for geopolymer concrete. To enhance the ductility, failure mode, and toughness of the geopolymer, researchers have added crumb rubber processed from scrap tires as partial replacement to fine aggregate of the geopolymer. Therefore, this study aims to develop rubberized geopolymer concrete (RGC) by partially replacing the fine aggregate with crumb rubber (CR). To optimize the mechanical properties of RGC, response surface methodology (RSM) has been used to develop 13 mixes with different levels and proportions of CR (10–30% partial replacement of fine aggregate by volume) and sodium hydroxide molarity (10–14 M) as input variables. The results showed that the strength properties increased as the molarity of NaOH increased, while the opposite trend was observed with CR. The maximum values for compressive strength, flexural strength, and uniaxial tensile strength were found to be 25 MPa, 3.1 MPa, and 0.41 MPa, respectively. Response surface models of the mechanical strengths, which were validated using ANOVA with high R2 values of 72–99%, have been developed. It has been found that using 10% CR with 14 M sodium hydroxide resulting in the best mechanical properties for RGC, which was validated with experimental tests. The result of the multi-objective optimization indicated that the optimum addition level for NaOH is 14 M, and the fine aggregate replacement level with CR is 10% in order to achieve a rubberized geopolymer suitable for structural applications.

Despite the excellent ductility and energy absorption properties of engineered cementitious composites (ECCs), a low modulus of elasticity and excessive drying shrinkage remain some of its major disadvantages. With the current trend in the application of nanotechnology in cementitious composites research, the effect of graphene oxide (GO) on the properties of ECCs is yet to be fully investigated, despite its promising results in ordinary cement paste, mortar, and concrete. ECCs need extensive material tailoring to provide the required mechanical characteristics and controlled fracture size with strain-hardening behavior. Striking a balance between these crucial hardened aspects of ECC without compromising any desired properties is a challenge. Hence, the main aim of the study reported in this paper is to use the response surface methodology (RSM) multi-objective optimization technique to identify an appropriate GO content via the weight of cement and also the volume fraction of polyvinyl alcohol (PVA) fiber as input variables that positively impact ECCs’ properties. Using RSM’s central composite design (CCD), 13 mixtures of various combinations of the variables (GO: 0.05%, 0.065%, 0.08%; PVA: 1%, 1.5%, 2%) were developed. Six responses were studied, including compressive strength, direct tensile strength, tensile capacity, flexural strength, modulus of elasticity, Poisson’s ratio, and drying shrinkage. Moreover, the microstructural properties of the composites were assessed using field-emission scanning electron microscopy (FESEM) and mercury intrusion porosimetry (MIP). The outcomes revealed that all the properties of ECCs were significantly enhanced by adding an optimum amount of 0.05% GO and 1–1.5% PVA fiber volume fractions. A maximum increase in 30%, 35%, 49%, and 33.9% in the compressive strength, direct tensile strength, flexural strength, and modulus of elasticity, respectively, of the mixes with 0.05% GO addition was recorded. It is demonstrated that the use of 0.05% GO as a nanoscale particle can provide good outputs for the construction industry.

Engineered Cementitious Composites (ECC) are widely used in various structures due to their high strength, durability, and ductility. However, they are still vulnerable to environmental factors such as sulphate and acid attack. These attacks damage the concrete matrix, which leads to cracking and corrosion of the reinforcing steel. To mitigate these issues, various techniques have been developed, including the addition of graphene oxide to the ECC mix. Graphene oxide has shown potential in improving the mechanical properties and durability of ECC. The purpose of this study was to use multi-objective optimization to identify an appropriate GO by the weight of the cement and polyvinyl alcohol (PVA) fiber volume fraction in an ECC mixture. Using RSM’s central composite design (CCD), thirteen mixtures of various possible combinations of variables (GO: 0.05 percent to 0.08 percent, PVA: 1–2 percent) were established, and eight response responses (compressive strength, change in length, weight loss, pH test, weight gain, expansion, rapid chloride permeability test and water absorption) were examined. However, analysis of variance was used to effectively design and evaluate eight (six quadratic and two linear) response models. All the models had extremely high R2 values, ranging from 84 percent to 99 percent. The multi-objective optimization produced ideal variable values (GO: 0.05 percent and PVA: 1%) and projected optimum response values. The predicted values were verified experimentally and found to correlate extremely well with the experimental data, with less than a 5% error. The outcome showed that the maximum increase of 30% in the compressive strength was recorded at 0.05% of GO as a nanomaterial in ECC. In addition, the expansion due to sulfate resistance and change in length due to acid attack were decreased by 0.0023% and 0.28%, respectively, when the use of 0.08% of GO as a nanomaterial in the ECC matrix was reinforced with 1% PVA fiber for 28 days. Moreover, the weight loss and weight gain of ECC combined with 1% of PVA fiber due to chemical attack decreased by 66.70% and 77.80%, respectively, at 0.08% of GO as a nanoscale particle than that of the reference mix for 28 days. In addition, the pH value due to acid attack, rapid chloride permeability test value, water absorption, and slump flow of the fresh mixture were decreased as the concentration of GO rose in ECC. The results indicated that the incorporation of 0.05% GO as a nanomaterial and 1 to 1.5% of PVA fiber will provide the best outcomes for the construction industry.

Chemical deterioration, including sulphate and acid attacks, is a major issue affecting the long-term durability of engineered cementitious composite (ECC) constructions that contact water from various sources, including groundwater, seawater, sewer water, and drinking water. This research enhances ECCs’ strength and resilience against chemical attack by combining cementitious composites with multiwalled carbon nanotubes (CNTs) and polyvinyl alcohol (PVA) fibre volume fractions using multiobjective optimization. The central composite design (CCD) of RSM was applied to generate thirteen mixes of different potential combinations of factors (multiwalled CNTs: 0.05% to 0.08%, PVA: 1–2%) and eight outcome responses were studied, although eight response models—six quadratic and two linear—were successfully designed and assessed using analysis of variance. The coefficients associated with R2 for all the models were exceptionally high, with values varying from 84 to 99 percent. The multiobjective optimization predicted the best outcomes and developed optimal values for both variables (CNTs: 0.05% and PVA: 1%). The results showed that, at 0.05% of CNTs in ECCs, an ultimate improvement of 23% in compressive strength was seen. Additionally, when CNTs are used to grow in the ECC matrix, the expansion owing to sulphate resistance and length change due to acid attack are both reduced. In addition, when the percentage of CNTs increases in ECCs, the weight loss and pH value owing to acid attack, as well as the rate of chloride permeability test results, are reduced. Furthermore, CNTs and PVA fibres with 0.05% and 1–1.5% concentrations offer optimal construction sector outcomes.

Supplementary cementitious materials (SCMs) are strongly advised as an alternative to cement to reduce its adverse environmental effects. One such SCMs is palm oil fuel ash (POFA), a waste material generated in large quantities in Southeast Asian countries, and there is insufficient data on its use in engineered cementitious composite (ECC). This study aims to optimize the properties of ECC using POFA as a cement replacement, by using 13 mixes developed by response surface methodology (RSM) with the POFA (at 20, 30, and 40% cement replacement levels) and PVA fiber (at 1, 1.5, and 2% volume fractions) as the input factors. The compressive, tensile, and flexural strengths, and tensile capacity (CS, TS, FS, and TC) were assessed. The microstructural properties were determined using Field-Emission Scanning Electron Microscopy (FESEM) and Mercury Intrusion Porosimetry (MIP). Results indicated that while the ductility and strain capacity increased with POFA, the strengths decreased by up to 51.5%. However, a structural POFA-ECC could be made with up to 30% POFA and 1–5% PVA fiber. The RSM optimization revealed 27.68% POFA and 2% PVA fiber as the optimal levels of the input factors, with the experimental validation correlating with the predicted values at less than 10% error.

Severe damage or collapse of reinforced concrete dapped-end beams (RC-DEBs) may occur during the service life. The collapse of the Concorde overpass structure in Laval, Quebec, Canada, in 2006 revealed the causes of collapse, i.e., insufficient shear strength (no stirrups), misplacement of hanger reinforcement, etc. In addition, the inspection report of RC half-joint bridges in England until 2018 expressed that damages or failures of half-joint structures can be attributed to the poor condition of structures or non-compliant reinforcement detailing. These conditions exhibited how important it is to understand the proper detailing of dapped-end reinforcement. To date, some studies have performed investigations on the effect of inadequate dapped-end reinforcement on the structural behavior of DEBs. However, the results of the research to date were not yet complete enough to disclose the role of each group of dapped-end reinforcement in resisting the working load. Therefore, this study was carried out to investigate the main role of each group of dapped-end reinforcement separately on the structural performance of RC-DEBs. Eight large-scaled RC-DEBs (with sizes of 1800 mm length, 120 mm width and 250 mm height) were prepared, cast and cured. All DEB specimens were tested under the three-point loading up to failure. To localize the effect of shear failure, the shear span-depth ratio (av⁄d) of 1.43 was set. Test results exhibited that arrangement of a specific group of dapped-end reinforcement separately affects the structural performance of DEBs significantly. The diagonal reinforcement (DR) group was found to be more effective than the vertical hanger reinforcement (HR) group. The failure load capacity of the DR group (DEB-18) achieved 0.29 times that of the control beam (DEB-3). Meantime, the nib flexure reinforcement (NFR) group demonstrated the most important role in the structural performance of DEBs compared to other dapped-end reinforcement groups. The failure load capacity of the NFR group (DEB-39) reached 0.62 times that of the control beam, while rupture deflection of the NFR group also exhibited the highest value than other groups, i.e., 0.62 times that of the control beam. In addition, analysis results of rosette strain gages (RSGs) data indicated that regions near to re-entrant corner and its vicinity experienced the highest stress concentration factor (SCF) compared to other places of the beams. These regions were more susceptible to experiencing the first crack, progressive crack, damage or failure first than other regions of DEBs. The greater the value of SCF, the greater the probability of collapse occurring in the related structural elements, which is also followed by a lower failure load capacity. DEB-1 (without dapped-end reinforcement) has the highest SCF (205.68), and the lowest failure load capacity (12.58 kN), whilst DEB-3 (with the complete dapped-end reinforcement) has the lowest SCF (79.62), but the highest failure load capacity (105.26 kN). Obviously, DEB-3 can withstand the working load properly. Its adequate dapped-end reinforcement is able to accommodate and distribute the high stress flows in the dapped-end region properly, which causes the SCF value to decrease.

The existence of soft soil in offshore areas may lead to the amplification of vibration received from offshore facilities, especially from the existing fixed-jacket platforms, which were designed without provision to seismicity, as in Malaysian water. Therefore, this study was designed to develop a seismic microzonation map and a soil amplification factor map according to soil type; we propose horizontal response spectra and site coefficient values (Ca and Cv) for the Malay Basin. A one-dimensional nonlinear analysis of layered soil (NERA) was used in the ground response analysis for six selected seismic events under five return periods of 100, 200, 500, 1000, and 2500 years. Soil amplification factors for soil types D and E showed a decreasing trend from 100 years to 2500 years. Two designed horizontal response spectra are proposed (for soil type D and E) under average and envelope conditions; a comparison with ISO showed that the proposed spectra were higher, especially for soil type E. To summarize, the seismicity effect should be included in the development of offshore industries as findings indicated that soil amplification occurred in soil types D and E at the Malay Basin.

The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are production-based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in-depth integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered.