Journal of Civil Engineering, Science and Technology

EDITORIAL NOTES: APPLICATION OF COMPUTATIONAL/ARTIFICIAL INTELLIGENCE IN CONCRETE STRUCTURES AND MATERIALS

2024-04-29T12:24:11+00:00

In recent years, the application of computational/artificial intelligence (CI/AI) in concrete structures and materials has gained popularity as evident in the search in the SCOPUS database using these keywords. The integration of CI/AI in concrete structures marks a significant advancement in civil engineering, offering innovative solutions across various stages of infrastructure development. From analysis and design to construction, monitoring, and maintenance, CI/AI technologies revolutionize traditional practices, enabling engineers to optimize concrete structures for enhanced performance, durability, and sustainability. Material design and optimization in concrete have also been propelled forward by CI/AI technologies. Traditional methods rely on trial-and-error, but CI/AI models analyze vast datasets to identify optimal mixtures with superior properties and resistance to environmental factors. Utilization of supplementary cementitious materials and industrial wastes introduces complexities, addressed by CI/AI's predictive capabilities for short- and long-term concrete properties. By minimizing waste and energy consumption, CI/AI-driven design fosters environmentally friendly formulations while ensuring structural integrity. Overall, CI/AI enhances prediction, optimization, and monitoring, ushering in a new era of resilient and sustainable concrete materials. These advancements also contribute to sustainable development by optimizing material usage, reducing waste, and improving energy efficiency, underscoring CI/AI's transformative potential in shaping the future of concrete structures and materials.

CORN-COB ASH AS PARTIAL REPLACEMENT OF CEMENT FOR STABILIZATION OF LATERITE SOIL

2024-04-29T12:25:21+00:00

Properties of underlying soils and borrowed soil samples are some of the key factors that determine the performance rate of roads. Most of the underlying soils possess some characteristics that make them unsuitable for use. There are available agricultural waste products in most rural settlements which can be used to treat unsuitable soils. This research examined the use of corn cob ash (CCA) as an admixture to cement on some selected geotechnical properties of laterite soil. The choice of the A-7-5 class of laterite soil is due to its general rating as poor material for subgrade and other layers of road pavement by the classification system of the American Association of State Highway and Transportation Officials (AASHTO). Cement was gradually added to the soil sample in steps of 2% from 0% to 10% by weight of the soil sample and its effect on the plasticity of the sample was examined. The addition of cement performed optimally on the soil’s plasticity at 4% which was used to form different mixtures of cement and CCA having a total sum not exceeding 4%. The additives were added to the soil sample which was subjected to laboratory tests such as compaction, California bearing ratio (CBR) and unconfined compressive strength (UCS) compacted with the efforts of 596kN/m² and 1192kN/m². The combination of 2% cement and 2% CCA on the soil sample improved the plasticity index and UCS properties of the soil to its optimal level while 3-1 and 4-0 cement-CCA performed optimally for CBR and compaction respectively. Thus, it was concluded that CCA performed optimally with cement at a ratio varying between 4:0 to 3:1 total percentage not exceeding 4% of the weight of the soil sample.

A COMPARATIVE STUDY OF CATBOOST AND ARTIFICIAL NEURAL NETWORKS IN ENHANCING TRIP GENERATION MODELLING FOR ILORIN CITY

2024-04-29T12:24:58+00:00

Trip generation plays a crucial role in transportation planning, and the choice of an appropriate model is essential for predicting future travel patterns. This study focuses on comparing the suitability and performance of CatBoost and ANN for trip generation (production and attraction) modelling of Ilorin City. By incorporating Ilorin household and trip characteristics, population data, and maps, this study evaluates the performance of the models. The two models demonstrated high accuracy and performance. In terms of trip production, the CatBoost model displayed exceptional accuracy, attaining an R-squared value of 0.99999992016446, accompanied by an impressively low mean squared error (MSE) of 3.93870930136429e-05. In contrast, the neural network exhibited a slightly lower accuracy of 0.999873850524181, with an error value of 0.0581313408911228. Similarly, for trip attraction, the CatBoost model showcased remarkable accuracy and precision, achieving an accuracy score of 0.9999999999999994 and an extremely low error value of 2.26762031965784e-13. The neural network model demonstrated an accuracy of 0.99999999990335 and a negligible error value of 0.000000041994. These findings underscore the strong predictive capabilities of both models for trip production and attraction, with the CatBoost model notably excelling in achieving nearly flawless accuracy and minimal error values across both aspects in Ilorin. Further research can explore the application of other advanced machine-learning techniques and combine their strengths to enhance the accuracy and robustness of trip-generation models.

EXPLORING THE POTENTIAL OF RED ASH AND MARBLE CHIPS AS ALTERNATIVES IN BASE COURSE MATERIAL VIA BLENDING WITH CRUSHED STONE AGGREGATE

2024-04-29T12:24:35+00:00

Despite its abundance, red ash has compaction issues owing to its lightweight, rough circular surface, and high porosity. The usage of conventional base course materials across the country incurs shipping expenses and takes time, slowing down projects because they are only widely available in limited areas across the country. In this study, non-probably sampling approach was used to examine the potential for employing red ash and marble chips as an alternative base course material by mixing them with crushed stone aggregate (CSA). In order to accomplish the goal of this study, an experimental test was conducted via trial and error, focusing on the mechanical stabilization of red ash and marble chips. Their physical characteristics were subsequently assessed through laboratory testing. In the laboratory, nine (9) samples of red ash and marble chips blended with CSA in varying percentages (0, 5, 10, 15, 20, 25, and 30 percent) were examined. The laboratory test results showed that 100% red ash and marble chips gave; CBR, SG, AIV, ACV, LAA, FI, EI, water absorption, and soundness: 55.4%, 2.38%, 18.20%, 26.34%, 19.69%, 5.59%, 12.09%, 2.5% and 13.80 and 83.6%, 2.63%, 19.70%, 26.41%, 19.75%, 23.2%, 16.9%, 1.24% and 12.22 respectively. Several of these test results align with the Ethiopian Road Authority (ERA) standard specifications; however, the findings from the CBR, water absorption, and soundness tests do not. Thus, mechanical stabilization was employed to improve the samples' physical characteristics. Experimental results are obtained by mixing 20% red ash, 20% marble chips, and 60% CSA. The values for CBR, SG, AIV, ACV, LAA, FI, EI, water absorption, and soundness are 102.5%, 2.55, 14.23%, 19.84%, 7.92%, 18.61%, 20.77%, 0.83%, and 7.38, respectively. The CBR, water absorption, and soundness characteristics all meet the necessary ERA standard specifications for crushed weathered rock (GB2) and natural coarsely graded granular materials (GB3) at this particular proportion. Hence, it is recommended to incorporate red ash up to 20% and marble chips up to 20% by weight, alongside 60% CSA, for constructing road base courses, particularly when these materials are reasonably accessible from construction sites and are widely available in the area.

EVALUATION OF LOCUST BEAN POD ASH AS MINERAL FILLER IN HOT MIX ASPHALT

2024-04-29T12:23:45+00:00

An increase in the consumption of agricultural products generates large quantities of waste daily. The husks of the locust bean seeds when removed from the plant are littered in the environment which negatively affects the environment. In this research, locust bean pod ash (LBPA) was used as a mineral filler in hot mix asphalt. Physical and chemical tests were done on the aggregate, bitumen and LBPA, showing adequacy for use in asphalt concrete production. All tests were conducted in accordance with relevant standards. LBPA was admixed with granite dust from 0–50% (at intervals of 10%) with varying bitumen content from 4–7% (at 0.5% intervals). For this experiment, the Marshall mix design method was used. The Marshall stability of samples containing LBPA improved by 19%, from 8.16kN to 9.67kN. Similarly, Marshall flow decreased by 19% from 3.4 mm to 2.75 mm. The density-void analysis of the asphalt samples also revealed an improvement. The microstructural examination revealed an enhanced structural arrangement due to the flocculation of the LBPA particles. Overall, the hot mix asphalt samples meet the Federal Ministry of Works and Housing specifications for flexible pavement-wearing courses. It was determined by the study that adding 40% LBPA with 60% granite dust at 5% bitumen content would improve the performance of hot mix asphalt.

RECYCLING OF ASPHALT PAVEMENT AGGREGATES AND WASTE PLASTIC BOTTLES IN ADDITION TO HOT-MIX ASPHALT PRODUCTION: ADEQUATE RECYCLING RATE

2024-04-29T12:22:25+00:00

This study focused on the recycling of asphalt pavement aggregates and waste plastic bottles (WPB) in addition to hot-mix asphalt (HMA) production. To achieve this objective, non-probable sampling methods were used to gather samples from the study locations. Crushed stone aggregate (CSA), bitumen, mineral filler, reclaimed asphalt pavement aggregate (RAPA), and WPB were the ingredients employed based on the requirements in the standard specification for asphalt concrete production. Initially, the Marshall Stability Test was then carried out using CSA with 6% and bitumen levels of 4.0, 4.5, 5.0, 5.5, and 6% by weight of the total mix to ascertain what the bitumen content should be with RAPA with replacement rates of 10, 20, and 30% and that of WPB with 2, 6, 8, 10, 12, and 14%. In the Marshall Stability Test, which consisted of three trials of 195 samples and 60 mix designs, 45 were for the control mix and 150 for the replacement proportion. In the Marshall Stability Test, the ideal value for CSA was 5.1%; for RAPA, 5.1%; and for WPB, 7.7, 5.5, 5.4, 5.0, 5.5, and 5.4% optimum bituminous content (OBC). 20% RAPA and 10% WPB by weight of OBC in the stability-modified asphalt mix satisfies Ethiopian Road Authority (ERA) and American Society for Testing and Material (ASTM) specifications for all qualities tested. Finally, for improved asphalt mix performance, a combined 70% CSA, 20% RAPA, and 10% WPB should be used in asphalt mixes at 5.0% OBC. These experimental Marshal Stability Test findings satisfy the necessary specifications of ERA and ASTM for all tests used in HMA production. Thus, this proportion is strongly advised.

A NOVEL APPROACH OF NON-DESTRUCTIVE TESTING FOR W-BEAM GUARDRAIL DEFECT DETECTION USING SURFACE WAVE TECHNOLOGY

2024-04-29T12:23:18+00:00

The W-beam guardrail, alternatively referred to as a guardrail or railing, constitutes a system meticulously engineered to prevent vehicular deviation into hazardous or restricted zones. Such mechanisms are ubiquitously employed across major global thoroughfares. However, vehicular collisions can inflict considerable damage upon the W-beam, thereby compromising its structural integrity. Concurrently, subpar maintenance further exacerbates this issue, potentially transforming the W-beam from an accident prevention tool into a catalyst for mishaps. Hence, the implementation of an effective maintenance and inspection strategy is imperative. The research presented herein proposes a novel method for W-beam defect detection, leveraging surface wave technology. A pulse-echo setup has been established, employing a 10-cycle tone burst to operate at a centre frequency of 48 kHz as the transmission medium. Signals reflected from the defective specimen were subsequently analysed; however, it was observed that this technique was unable to detect all defects. Despite these limitations, the study delineates the potential to pinpoint the precise location of physical hole defects. This was achieved through the time-of-flight method and leveraging known material characteristics, such as the speed of sound, at 5 cm between the hole and transmitter. The researchers predict that this methodology, when augmented with a higher voltage and a suitable amplifier circuit for detection, could be utilized for long-distance defect detection. This opens up new avenues for ensuring the structural integrity of W-beam guardrails, thereby enhancing road safety.

GEOTECHNICAL AND MINERALOGICAL CHARACTERISTICS OF LATERITIC SOIL AND LOCUST BEAN (PARKIA BIGLOBOSA) PODS ASH AS CONSTRUCTION SOIL

2024-04-29T12:22:50+00:00

African locust bean (Parkia Biglobosa) has been in great demand and consequently yielding large amounts of the pods which could be a potential hazard to the environment. Converting the pods to ash that would be utilized as construction material is another way of handling them properly. The effects of the mineralogy of the soil on its geotechnical characteristics when the soil is treated with locust bean pod ash have been given very little attention. Therefore, this study considered the effects of locust bean pod ash (LBPA) and the mineralogy of lateritic soil on some geotechnical properties necessary for it to be used as subgrade or foundation soil. The determination of the chemical constituents of the LBPA was carried out using Atomic Absorption Spectrometer. The lateritic soil was characterized by identifying the clay minerals as well as non-clay minerals present in the soil using the x-ray diffraction technique and preliminary tests were also carried out to properly rate the soil. The soil was treated by applying dosages of LBPA 0 – 25% at intervals of 5% and the percentages were measured by weight of the dry soil. The tests conducted on the prepared soil samples with LBPA were specific gravity, consistency indices, compaction (British Standard Light), California bearing ratio and direct shear box test. The LBPA was found to be an acceptable pozzolanic material for the treatment of the lateritic soil. The lateritic soil was observed to belong to soil groups A-2-6(0) in the AASHTO and poorly graded sand (SP) in USCS ratings. It was also observed that the non-clay minerals present in the soil were quartz, feldspar and mica whereas the clay minerals present were kaolinite, iolite, vermiculite and chlorite which had a significant influence on the geotechnical characteristics of the soil. The specific gravity of the LBPA was relatively low and the additions of LBPA reduced the specific gravity of the soil. The consistency indices also dropped and subsequently increased with the further addition of LBPA. The increase in LBPA contents caused increments in the optimum moisture content while the maximum dry density and shear strength parameters reduced. The strength characteristics like the California Bearing Ratio of the lateritic soil improved by 173.91% at the addition of 5% LBSA content which was determined to be the optimum dosage.